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Sample records for locally weighted regression

  1. SPE dose prediction using locally weighted regression

    International Nuclear Information System (INIS)

    Hines, J. W.; Townsend, L. W.; Nichols, T. F.

    2005-01-01

    When astronauts are outside earth's protective magnetosphere, they are subject to large radiation doses resulting from solar particle events (SPEs). The total dose received from a major SPE in deep space could cause severe radiation poisoning. The dose is usually received over a 20-40 h time interval but the event's effects may be mitigated with an early warning system. This paper presents a method to predict the total dose early in the event. It uses a locally weighted regression model, which is easier to train and provides predictions as accurate as neural network models previously used. (authors)

  2. SPE dose prediction using locally weighted regression

    International Nuclear Information System (INIS)

    Hines, J. W.; Townsend, L. W.; Nichols, T. F.

    2005-01-01

    When astronauts are outside Earth's protective magnetosphere, they are subject to large radiation doses resulting from solar particle events. The total dose received from a major solar particle event in deep space could cause severe radiation poisoning. The dose is usually received over a 20-40 h time interval but the event's effects may be reduced with an early warning system. This paper presents a method to predict the total dose early in the event. It uses a locally weighted regression model, which is easier to train, and provides predictions as accurate as the neural network models that were used previously. (authors)

  3. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

    Science.gov (United States)

    Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong

    2018-01-01

    Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

  4. A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

    Directory of Open Access Journals (Sweden)

    Xu Yu

    2018-01-01

    Full Text Available Cross-domain collaborative filtering (CDCF solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR. We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

  5. Mapping the results of local statistics: Using geographically weighted regression

    Directory of Open Access Journals (Sweden)

    Stephen A. Matthews

    2012-03-01

    Full Text Available BACKGROUND The application of geographically weighted regression (GWR - a local spatial statistical technique used to test for spatial nonstationarity - has grown rapidly in the social, health, and demographic sciences. GWR is a useful exploratory analytical tool that generates a set of location-specific parameter estimates which can be mapped and analysed to provide information on spatial nonstationarity in the relationships between predictors and the outcome variable. OBJECTIVE A major challenge to users of GWR methods is how best to present and synthesize the large number of mappable results, specifically the local parameter parameter estimates and local t-values, generated from local GWR models. We offer an elegant solution. METHODS This paper introduces a mapping technique to simultaneously display local parameter estimates and local t-values on one map based on the use of data selection and transparency techniques. We integrate GWR software and GIS software package (ArcGIS and adapt earlier work in cartography on bivariate mapping. We compare traditional mapping strategies (i.e., side-by-side comparison and isoline overlay maps with our method using an illustration focusing on US county infant mortality data. CONCLUSIONS The resultant map design is more elegant than methods used to date. This type of map presentation can facilitate the exploration and interpretation of nonstationarity, focusing map reader attention on the areas of primary interest.

  6. On Weighted Support Vector Regression

    DEFF Research Database (Denmark)

    Han, Xixuan; Clemmensen, Line Katrine Harder

    2014-01-01

    We propose a new type of weighted support vector regression (SVR), motivated by modeling local dependencies in time and space in prediction of house prices. The classic weights of the weighted SVR are added to the slack variables in the objective function (OF‐weights). This procedure directly...... shrinks the coefficient of each observation in the estimated functions; thus, it is widely used for minimizing influence of outliers. We propose to additionally add weights to the slack variables in the constraints (CF‐weights) and call the combination of weights the doubly weighted SVR. We illustrate...... the differences and similarities of the two types of weights by demonstrating the connection between the Least Absolute Shrinkage and Selection Operator (LASSO) and the SVR. We show that an SVR problem can be transformed to a LASSO problem plus a linear constraint and a box constraint. We demonstrate...

  7. Fungible weights in logistic regression.

    Science.gov (United States)

    Jones, Jeff A; Waller, Niels G

    2016-06-01

    In this article we develop methods for assessing parameter sensitivity in logistic regression models. To set the stage for this work, we first review Waller's (2008) equations for computing fungible weights in linear regression. Next, we describe 2 methods for computing fungible weights in logistic regression. To demonstrate the utility of these methods, we compute fungible logistic regression weights using data from the Centers for Disease Control and Prevention's (2010) Youth Risk Behavior Surveillance Survey, and we illustrate how these alternate weights can be used to evaluate parameter sensitivity. To make our work accessible to the research community, we provide R code (R Core Team, 2015) that will generate both kinds of fungible logistic regression weights. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  8. Robust Locally Weighted Regression For Ground Surface Extraction In Mobile Laser Scanning 3D Data

    Directory of Open Access Journals (Sweden)

    A. Nurunnabi

    2013-10-01

    Full Text Available A new robust way for ground surface extraction from mobile laser scanning 3D point cloud data is proposed in this paper. Fitting polynomials along 2D/3D points is one of the well-known methods for filtering ground points, but it is evident that unorganized point clouds consist of multiple complex structures by nature so it is not suitable for fitting a parametric global model. The aim of this research is to develop and implement an algorithm to classify ground and non-ground points based on statistically robust locally weighted regression which fits a regression surface (line in 2D by fitting without any predefined global functional relation among the variables of interest. Afterwards, the z (elevation-values are robustly down weighted based on the residuals for the fitted points. The new set of down weighted z-values along with x (or y values are used to get a new fit of the (lower surface (line. The process of fitting and down-weighting continues until the difference between two consecutive fits is insignificant. Then the final fit represents the ground level of the given point cloud and the ground surface points can be extracted. The performance of the new method has been demonstrated through vehicle based mobile laser scanning 3D point cloud data from urban areas which include different problematic objects such as short walls, large buildings, electric poles, sign posts and cars. The method has potential in areas like building/construction footprint determination, 3D city modelling, corridor mapping and asset management.

  9. Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression

    Science.gov (United States)

    Martínez-Fernández, J.; Chuvieco, E.; Koutsias, N.

    2013-02-01

    Humans are responsible for most forest fires in Europe, but anthropogenic factors behind these events are still poorly understood. We tried to identify the driving factors of human-caused fire occurrence in Spain by applying two different statistical approaches. Firstly, assuming stationary processes for the whole country, we created models based on multiple linear regression and binary logistic regression to find factors associated with fire density and fire presence, respectively. Secondly, we used geographically weighted regression (GWR) to better understand and explore the local and regional variations of those factors behind human-caused fire occurrence. The number of human-caused fires occurring within a 25-yr period (1983-2007) was computed for each of the 7638 Spanish mainland municipalities, creating a binary variable (fire/no fire) to develop logistic models, and a continuous variable (fire density) to build standard linear regression models. A total of 383 657 fires were registered in the study dataset. The binary logistic model, which estimates the probability of having/not having a fire, successfully classified 76.4% of the total observations, while the ordinary least squares (OLS) regression model explained 53% of the variation of the fire density patterns (adjusted R2 = 0.53). Both approaches confirmed, in addition to forest and climatic variables, the importance of variables related with agrarian activities, land abandonment, rural population exodus and developmental processes as underlying factors of fire occurrence. For the GWR approach, the explanatory power of the GW linear model for fire density using an adaptive bandwidth increased from 53% to 67%, while for the GW logistic model the correctly classified observations improved only slightly, from 76.4% to 78.4%, but significantly according to the corrected Akaike Information Criterion (AICc), from 3451.19 to 3321.19. The results from GWR indicated a significant spatial variation in the local

  10. AN INVESTIGATION OF LOCAL EFFECTS ON SURFACE WARMING WITH GEOGRAPHICALLY WEIGHTED REGRESSION (GWR

    Directory of Open Access Journals (Sweden)

    Y. Xue

    2012-07-01

    Full Text Available Urban warming is sensitive to the nature (thermal properties, including albedo, water content, heat capacity and thermal conductivity and the placement (surface geometry or urban topography of urban surface. In the literature the spatial dependence and heterogeneity of urban thermal landscape is widely observed based on thermal infrared remote sensing within the urban environment. Urban surface warming is conceived as a big contribution to urban warming, the study of urban surface warming possesses significant meaning for probing into the problem of urban warming.The urban thermal landscape study takes advantage of the continuous surface derived from thermal infrared remote sensing at the landscape scale, the detailed variation of local surface temperature can be measured and analyzed through the systematic investigation. At the same time urban environmental factors can be quantified with remote sensing and GIS techniques. This enables a systematic investigation of urban thermal landscape with a link to be established between local environmental setting and surface temperature variation. The goal of this research is utilizing Geographically Weighted Regression (GWR to analyze the spatial relationship between urban form and surface temperature variation in order to clarify the local effects on surface warming, moreover to reveal the possible dynamics in the local influences of environmental indicators on the variation of local surface temperature across space and time. In this research, GWR analysis proved that the spatial variation in relationships between environmental setting and surface temperature was significant with Monte Carlo significance test and distinctive in day-night change. Comparatively, GWR facilitated the site specific investigation based on local statistical technique. The inference based on GWR model provided enriched information regarding the spatial variation of local environment effect on surface temperature variation which

  11. Geographically weighted regression and multicollinearity: dispelling the myth

    Science.gov (United States)

    Fotheringham, A. Stewart; Oshan, Taylor M.

    2016-10-01

    Geographically weighted regression (GWR) extends the familiar regression framework by estimating a set of parameters for any number of locations within a study area, rather than producing a single parameter estimate for each relationship specified in the model. Recent literature has suggested that GWR is highly susceptible to the effects of multicollinearity between explanatory variables and has proposed a series of local measures of multicollinearity as an indicator of potential problems. In this paper, we employ a controlled simulation to demonstrate that GWR is in fact very robust to the effects of multicollinearity. Consequently, the contention that GWR is highly susceptible to multicollinearity issues needs rethinking.

  12. Parameter estimation and statistical test of geographically weighted bivariate Poisson inverse Gaussian regression models

    Science.gov (United States)

    Amalia, Junita; Purhadi, Otok, Bambang Widjanarko

    2017-11-01

    Poisson distribution is a discrete distribution with count data as the random variables and it has one parameter defines both mean and variance. Poisson regression assumes mean and variance should be same (equidispersion). Nonetheless, some case of the count data unsatisfied this assumption because variance exceeds mean (over-dispersion). The ignorance of over-dispersion causes underestimates in standard error. Furthermore, it causes incorrect decision in the statistical test. Previously, paired count data has a correlation and it has bivariate Poisson distribution. If there is over-dispersion, modeling paired count data is not sufficient with simple bivariate Poisson regression. Bivariate Poisson Inverse Gaussian Regression (BPIGR) model is mix Poisson regression for modeling paired count data within over-dispersion. BPIGR model produces a global model for all locations. In another hand, each location has different geographic conditions, social, cultural and economic so that Geographically Weighted Regression (GWR) is needed. The weighting function of each location in GWR generates a different local model. Geographically Weighted Bivariate Poisson Inverse Gaussian Regression (GWBPIGR) model is used to solve over-dispersion and to generate local models. Parameter estimation of GWBPIGR model obtained by Maximum Likelihood Estimation (MLE) method. Meanwhile, hypothesis testing of GWBPIGR model acquired by Maximum Likelihood Ratio Test (MLRT) method.

  13. Single image super-resolution using locally adaptive multiple linear regression.

    Science.gov (United States)

    Yu, Soohwan; Kang, Wonseok; Ko, Seungyong; Paik, Joonki

    2015-12-01

    This paper presents a regularized superresolution (SR) reconstruction method using locally adaptive multiple linear regression to overcome the limitation of spatial resolution of digital images. In order to make the SR problem better-posed, the proposed method incorporates the locally adaptive multiple linear regression into the regularization process as a local prior. The local regularization prior assumes that the target high-resolution (HR) pixel is generated by a linear combination of similar pixels in differently scaled patches and optimum weight parameters. In addition, we adapt a modified version of the nonlocal means filter as a smoothness prior to utilize the patch redundancy. Experimental results show that the proposed algorithm better restores HR images than existing state-of-the-art methods in the sense of the most objective measures in the literature.

  14. On weighted and locally polynomial directional quantile regression

    Czech Academy of Sciences Publication Activity Database

    Boček, Pavel; Šiman, Miroslav

    2017-01-01

    Roč. 32, č. 3 (2017), s. 929-946 ISSN 0943-4062 R&D Projects: GA ČR GA14-07234S Institutional support: RVO:67985556 Keywords : Quantile regression * Nonparametric regression * Nonparametric regression Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.434, year: 2016 http://library.utia.cas.cz/separaty/2017/SI/bocek-0458380.pdf

  15. Geographically weighted regression model on poverty indicator

    Science.gov (United States)

    Slamet, I.; Nugroho, N. F. T. A.; Muslich

    2017-12-01

    In this research, we applied geographically weighted regression (GWR) for analyzing the poverty in Central Java. We consider Gaussian Kernel as weighted function. The GWR uses the diagonal matrix resulted from calculating kernel Gaussian function as a weighted function in the regression model. The kernel weights is used to handle spatial effects on the data so that a model can be obtained for each location. The purpose of this paper is to model of poverty percentage data in Central Java province using GWR with Gaussian kernel weighted function and to determine the influencing factors in each regency/city in Central Java province. Based on the research, we obtained geographically weighted regression model with Gaussian kernel weighted function on poverty percentage data in Central Java province. We found that percentage of population working as farmers, population growth rate, percentage of households with regular sanitation, and BPJS beneficiaries are the variables that affect the percentage of poverty in Central Java province. In this research, we found the determination coefficient R2 are 68.64%. There are two categories of district which are influenced by different of significance factors.

  16. Mixed geographically weighted regression (MGWR) model with weighted adaptive bi-square for case of dengue hemorrhagic fever (DHF) in Surakarta

    Science.gov (United States)

    Astuti, H. N.; Saputro, D. R. S.; Susanti, Y.

    2017-06-01

    MGWR model is combination of linear regression model and geographically weighted regression (GWR) model, therefore, MGWR model could produce parameter estimation that had global parameter estimation, and other parameter that had local parameter in accordance with its observation location. The linkage between locations of the observations expressed in specific weighting that is adaptive bi-square. In this research, we applied MGWR model with weighted adaptive bi-square for case of DHF in Surakarta based on 10 factors (variables) that is supposed to influence the number of people with DHF. The observation unit in the research is 51 urban villages and the variables are number of inhabitants, number of houses, house index, many public places, number of healthy homes, number of Posyandu, area width, level population density, welfare of the family, and high-region. Based on this research, we obtained 51 MGWR models. The MGWR model were divided into 4 groups with significant variable is house index as a global variable, an area width as a local variable and the remaining variables vary in each. Global variables are variables that significantly affect all locations, while local variables are variables that significantly affect a specific location.

  17. Regression Equations for Birth Weight Estimation using ...

    African Journals Online (AJOL)

    In this study, Birth Weight has been estimated from anthropometric measurements of hand and foot. Linear regression equations were formed from each of the measured variables. These simple equations can be used to estimate Birth Weight of new born babies, in order to identify those with low birth weight and referred to ...

  18. Use of probabilistic weights to enhance linear regression myoelectric control.

    Science.gov (United States)

    Smith, Lauren H; Kuiken, Todd A; Hargrove, Levi J

    2015-12-01

    Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts' law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p linear regression control. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

  19. Scaling Flux Tower Observations of Sensible Heat Flux Using Weighted Area-to-Area Regression Kriging

    Directory of Open Access Journals (Sweden)

    Maogui Hu

    2015-07-01

    Full Text Available Sensible heat flux (H plays an important role in characterizations of land surface water and heat balance. There are various types of H measurement methods that depend on observation scale, from local-area-scale eddy covariance (EC to regional-scale large aperture scintillometer (LAS and remote sensing (RS products. However, methods of converting one H scale to another to validate RS products are still open for question. A previous area-to-area regression kriging-based scaling method performed well in converting EC-scale H to LAS-scale H. However, the method does not consider the path-weighting function in the EC- to LAS-scale kriging with the regression residue, which inevitably brought about a bias estimation. In this study, a weighted area-to-area regression kriging (WATA RK model is proposed to convert EC-scale H to LAS-scale H. It involves path-weighting functions of EC and LAS source areas in both regression and area kriging stages. Results show that WATA RK outperforms traditional methods in most cases, improving estimation accuracy. The method is considered to provide an efficient validation of RS H flux products.

  20. Geographically weighted negative binomial regression applied to zonal level safety performance models.

    Science.gov (United States)

    Gomes, Marcos José Timbó Lima; Cunto, Flávio; da Silva, Alan Ricardo

    2017-09-01

    Generalized Linear Models (GLM) with negative binomial distribution for errors, have been widely used to estimate safety at the level of transportation planning. The limited ability of this technique to take spatial effects into account can be overcome through the use of local models from spatial regression techniques, such as Geographically Weighted Poisson Regression (GWPR). Although GWPR is a system that deals with spatial dependency and heterogeneity and has already been used in some road safety studies at the planning level, it fails to account for the possible overdispersion that can be found in the observations on road-traffic crashes. Two approaches were adopted for the Geographically Weighted Negative Binomial Regression (GWNBR) model to allow discrete data to be modeled in a non-stationary form and to take note of the overdispersion of the data: the first examines the constant overdispersion for all the traffic zones and the second includes the variable for each spatial unit. This research conducts a comparative analysis between non-spatial global crash prediction models and spatial local GWPR and GWNBR at the level of traffic zones in Fortaleza/Brazil. A geographic database of 126 traffic zones was compiled from the available data on exposure, network characteristics, socioeconomic factors and land use. The models were calibrated by using the frequency of injury crashes as a dependent variable and the results showed that GWPR and GWNBR achieved a better performance than GLM for the average residuals and likelihood as well as reducing the spatial autocorrelation of the residuals, and the GWNBR model was more able to capture the spatial heterogeneity of the crash frequency. Copyright © 2017 Elsevier Ltd. All rights reserved.

  1. Use of probabilistic weights to enhance linear regression myoelectric control

    Science.gov (United States)

    Smith, Lauren H.; Kuiken, Todd A.; Hargrove, Levi J.

    2015-12-01

    Objective. Clinically available prostheses for transradial amputees do not allow simultaneous myoelectric control of degrees of freedom (DOFs). Linear regression methods can provide simultaneous myoelectric control, but frequently also result in difficulty with isolating individual DOFs when desired. This study evaluated the potential of using probabilistic estimates of categories of gross prosthesis movement, which are commonly used in classification-based myoelectric control, to enhance linear regression myoelectric control. Approach. Gaussian models were fit to electromyogram (EMG) feature distributions for three movement classes at each DOF (no movement, or movement in either direction) and used to weight the output of linear regression models by the probability that the user intended the movement. Eight able-bodied and two transradial amputee subjects worked in a virtual Fitts’ law task to evaluate differences in controllability between linear regression and probability-weighted regression for an intramuscular EMG-based three-DOF wrist and hand system. Main results. Real-time and offline analyses in able-bodied subjects demonstrated that probability weighting improved performance during single-DOF tasks (p < 0.05) by preventing extraneous movement at additional DOFs. Similar results were seen in experiments with two transradial amputees. Though goodness-of-fit evaluations suggested that the EMG feature distributions showed some deviations from the Gaussian, equal-covariance assumptions used in this experiment, the assumptions were sufficiently met to provide improved performance compared to linear regression control. Significance. Use of probability weights can improve the ability to isolate individual during linear regression myoelectric control, while maintaining the ability to simultaneously control multiple DOFs.

  2. Real-time prediction of respiratory motion based on local regression methods

    International Nuclear Information System (INIS)

    Ruan, D; Fessler, J A; Balter, J M

    2007-01-01

    Recent developments in modulation techniques enable conformal delivery of radiation doses to small, localized target volumes. One of the challenges in using these techniques is real-time tracking and predicting target motion, which is necessary to accommodate system latencies. For image-guided-radiotherapy systems, it is also desirable to minimize sampling rates to reduce imaging dose. This study focuses on predicting respiratory motion, which can significantly affect lung tumours. Predicting respiratory motion in real-time is challenging, due to the complexity of breathing patterns and the many sources of variability. We propose a prediction method based on local regression. There are three major ingredients of this approach: (1) forming an augmented state space to capture system dynamics, (2) local regression in the augmented space to train the predictor from previous observation data using semi-periodicity of respiratory motion, (3) local weighting adjustment to incorporate fading temporal correlations. To evaluate prediction accuracy, we computed the root mean square error between predicted tumor motion and its observed location for ten patients. For comparison, we also investigated commonly used predictive methods, namely linear prediction, neural networks and Kalman filtering to the same data. The proposed method reduced the prediction error for all imaging rates and latency lengths, particularly for long prediction lengths

  3. Estimation of Geographically Weighted Regression Case Study on Wet Land Paddy Productivities in Tulungagung Regency

    Directory of Open Access Journals (Sweden)

    Danang Ariyanto

    2017-11-01

    Full Text Available Regression is a method connected independent variable and dependent variable with estimation parameter as an output. Principal problem in this method is its application in spatial data. Geographically Weighted Regression (GWR method used to solve the problem. GWR  is a regression technique that extends the traditional regression framework by allowing the estimation of local rather than global parameters. In other words, GWR runs a regression for each location, instead of a sole regression for the entire study area. The purpose of this research is to analyze the factors influencing wet land paddy productivities in Tulungagung Regency. The methods used in this research is  GWR using cross validation  bandwidth and weighted by adaptive Gaussian kernel fungtion.This research using  4 variables which are presumed affecting the wet land paddy productivities such as:  the rate of rainfall(X1, the average cost of fertilizer per hectare(X2, the average cost of pestisides per hectare(X3 and Allocation of subsidized NPK fertilizer of food crops sub-sector(X4. Based on the result, X1, X2, X3 and X4  has a different effect on each Distric. So, to improve the productivity of wet land paddy in Tulungagung Regency required a special policy based on the GWR model in each distric.

  4. Time-localized wavelet multiple regression and correlation

    Science.gov (United States)

    Fernández-Macho, Javier

    2018-02-01

    This paper extends wavelet methodology to handle comovement dynamics of multivariate time series via moving weighted regression on wavelet coefficients. The concept of wavelet local multiple correlation is used to produce one single set of multiscale correlations along time, in contrast with the large number of wavelet correlation maps that need to be compared when using standard pairwise wavelet correlations with rolling windows. Also, the spectral properties of weight functions are investigated and it is argued that some common time windows, such as the usual rectangular rolling window, are not satisfactory on these grounds. The method is illustrated with a multiscale analysis of the comovements of Eurozone stock markets during this century. It is shown how the evolution of the correlation structure in these markets has been far from homogeneous both along time and across timescales featuring an acute divide across timescales at about the quarterly scale. At longer scales, evidence from the long-term correlation structure can be interpreted as stable perfect integration among Euro stock markets. On the other hand, at intramonth and intraweek scales, the short-term correlation structure has been clearly evolving along time, experiencing a sharp increase during financial crises which may be interpreted as evidence of financial 'contagion'.

  5. Face Alignment via Regressing Local Binary Features.

    Science.gov (United States)

    Ren, Shaoqing; Cao, Xudong; Wei, Yichen; Sun, Jian

    2016-03-01

    This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.

  6. On the Relationship Between Confidence Sets and Exchangeable Weights in Multiple Linear Regression.

    Science.gov (United States)

    Pek, Jolynn; Chalmers, R Philip; Monette, Georges

    2016-01-01

    When statistical models are employed to provide a parsimonious description of empirical relationships, the extent to which strong conclusions can be drawn rests on quantifying the uncertainty in parameter estimates. In multiple linear regression (MLR), regression weights carry two kinds of uncertainty represented by confidence sets (CSs) and exchangeable weights (EWs). Confidence sets quantify uncertainty in estimation whereas the set of EWs quantify uncertainty in the substantive interpretation of regression weights. As CSs and EWs share certain commonalities, we clarify the relationship between these two kinds of uncertainty about regression weights. We introduce a general framework describing how CSs and the set of EWs for regression weights are estimated from the likelihood-based and Wald-type approach, and establish the analytical relationship between CSs and sets of EWs. With empirical examples on posttraumatic growth of caregivers (Cadell et al., 2014; Schneider, Steele, Cadell & Hemsworth, 2011) and on graduate grade point average (Kuncel, Hezlett & Ones, 2001), we illustrate the usefulness of CSs and EWs for drawing strong scientific conclusions. We discuss the importance of considering both CSs and EWs as part of the scientific process, and provide an Online Appendix with R code for estimating Wald-type CSs and EWs for k regression weights.

  7. Local bilinear multiple-output quantile/depth regression

    Czech Academy of Sciences Publication Activity Database

    Hallin, M.; Lu, Z.; Paindaveine, D.; Šiman, Miroslav

    2015-01-01

    Roč. 21, č. 3 (2015), s. 1435-1466 ISSN 1350-7265 R&D Projects: GA MŠk(CZ) 1M06047 Institutional support: RVO:67985556 Keywords : conditional depth * growth chart * halfspace depth * local bilinear regression * multivariate quantile * quantile regression * regression depth Subject RIV: BA - General Mathematics Impact factor: 1.372, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/siman-0446857.pdf

  8. User's Guide to the Weighted-Multiple-Linear Regression Program (WREG version 1.0)

    Science.gov (United States)

    Eng, Ken; Chen, Yin-Yu; Kiang, Julie.E.

    2009-01-01

    Streamflow is not measured at every location in a stream network. Yet hydrologists, State and local agencies, and the general public still seek to know streamflow characteristics, such as mean annual flow or flood flows with different exceedance probabilities, at ungaged basins. The goals of this guide are to introduce and familiarize the user with the weighted multiple-linear regression (WREG) program, and to also provide the theoretical background for program features. The program is intended to be used to develop a regional estimation equation for streamflow characteristics that can be applied at an ungaged basin, or to improve the corresponding estimate at continuous-record streamflow gages with short records. The regional estimation equation results from a multiple-linear regression that relates the observable basin characteristics, such as drainage area, to streamflow characteristics.

  9. Modeling Fire Occurrence at the City Scale: A Comparison between Geographically Weighted Regression and Global Linear Regression.

    Science.gov (United States)

    Song, Chao; Kwan, Mei-Po; Zhu, Jiping

    2017-04-08

    An increasing number of fires are occurring with the rapid development of cities, resulting in increased risk for human beings and the environment. This study compares geographically weighted regression-based models, including geographically weighted regression (GWR) and geographically and temporally weighted regression (GTWR), which integrates spatial and temporal effects and global linear regression models (LM) for modeling fire risk at the city scale. The results show that the road density and the spatial distribution of enterprises have the strongest influences on fire risk, which implies that we should focus on areas where roads and enterprises are densely clustered. In addition, locations with a large number of enterprises have fewer fire ignition records, probably because of strict management and prevention measures. A changing number of significant variables across space indicate that heterogeneity mainly exists in the northern and eastern rural and suburban areas of Hefei city, where human-related facilities or road construction are only clustered in the city sub-centers. GTWR can capture small changes in the spatiotemporal heterogeneity of the variables while GWR and LM cannot. An approach that integrates space and time enables us to better understand the dynamic changes in fire risk. Thus governments can use the results to manage fire safety at the city scale.

  10. Factors affecting CO_2 emissions in China’s agriculture sector: Evidence from geographically weighted regression model

    International Nuclear Information System (INIS)

    Xu, Bin; Lin, Boqiang

    2017-01-01

    China is currently the world's largest emitter of carbon dioxide. Considered as a large agricultural country, carbon emission in China’s agriculture sector keeps on growing rapidly. It is, therefore, of great importance to investigate the driving forces of carbon dioxide emissions in this sector. The traditional regression estimation can only get “average” and “global” parameter estimates; it excludes the “local” parameter estimates which vary across space in some spatial systems. Geographically weighted regression embeds the latitude and longitude of the sample data into the regression parameters, and uses the local weighted least squares method to estimate the parameters point–by–point. To reveal the nonstationary spatial effects of driving forces, geographically weighted regression model is employed in this paper. The results show that economic growth is positively correlated with emissions, with the impact in the western region being less than that in the central and eastern regions. Urbanization is positively related to emissions but produces opposite effects pattern. Energy intensity is also correlated with emissions, with a decreasing trend from the eastern region to the central and western regions. Therefore, policymakers should take full account of the spatial nonstationarity of driving forces in designing emission reduction policies. - Highlights: • We explore the driving forces of CO_2 emissions in the agriculture sector. • Urbanization is positively related to emissions but produces opposite effect pattern. • The effect of energy intensity declines from the eastern region to western region.

  11. Weighted functional linear regression models for gene-based association analysis.

    Science.gov (United States)

    Belonogova, Nadezhda M; Svishcheva, Gulnara R; Wilson, James F; Campbell, Harry; Axenovich, Tatiana I

    2018-01-01

    Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.

  12. Regression with Sparse Approximations of Data

    DEFF Research Database (Denmark)

    Noorzad, Pardis; Sturm, Bob L.

    2012-01-01

    We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...

  13. Weight change in control group participants in behavioural weight loss interventions: a systematic review and meta-regression study

    Directory of Open Access Journals (Sweden)

    Waters Lauren

    2012-08-01

    Full Text Available Abstract Background Unanticipated control group improvements have been observed in intervention trials targeting various health behaviours. This phenomenon has not been studied in the context of behavioural weight loss intervention trials. The purpose of this study is to conduct a systematic review and meta-regression of behavioural weight loss interventions to quantify control group weight change, and relate the size of this effect to specific trial and sample characteristics. Methods Database searches identified reports of intervention trials meeting the inclusion criteria. Data on control group weight change and possible explanatory factors were abstracted and analysed descriptively and quantitatively. Results 85 trials were reviewed and 72 were included in the meta-regression. While there was no change in control group weight, control groups receiving usual care lost 1 kg more than control groups that received no intervention, beyond measurement. Conclusions There are several possible explanations why control group changes occur in intervention trials targeting other behaviours, but not for weight loss. Control group participation may prevent weight gain, although more research is needed to confirm this hypothesis.

  14. Weighted SGD for ℓp Regression with Randomized Preconditioning*

    Science.gov (United States)

    Yang, Jiyan; Chow, Yin-Lam; Ré, Christopher; Mahoney, Michael W.

    2018-01-01

    In recent years, stochastic gradient descent (SGD) methods and randomized linear algebra (RLA) algorithms have been applied to many large-scale problems in machine learning and data analysis. SGD methods are easy to implement and applicable to a wide range of convex optimization problems. In contrast, RLA algorithms provide much stronger performance guarantees but are applicable to a narrower class of problems. We aim to bridge the gap between these two methods in solving constrained overdetermined linear regression problems—e.g., ℓ2 and ℓ1 regression problems. We propose a hybrid algorithm named pwSGD that uses RLA techniques for preconditioning and constructing an importance sampling distribution, and then performs an SGD-like iterative process with weighted sampling on the preconditioned system.By rewriting a deterministic ℓp regression problem as a stochastic optimization problem, we connect pwSGD to several existing ℓp solvers including RLA methods with algorithmic leveraging (RLA for short).We prove that pwSGD inherits faster convergence rates that only depend on the lower dimension of the linear system, while maintaining low computation complexity. Such SGD convergence rates are superior to other related SGD algorithm such as the weighted randomized Kaczmarz algorithm.Particularly, when solving ℓ1 regression with size n by d, pwSGD returns an approximate solution with ε relative error in the objective value in 𝒪(log n·nnz(A)+poly(d)/ε2) time. This complexity is uniformly better than that of RLA methods in terms of both ε and d when the problem is unconstrained. In the presence of constraints, pwSGD only has to solve a sequence of much simpler and smaller optimization problem over the same constraints. In general this is more efficient than solving the constrained subproblem required in RLA.For ℓ2 regression, pwSGD returns an approximate solution with ε relative error in the objective value and the solution vector measured in

  15. Robust geographically weighted regression of modeling the Air Polluter Standard Index (APSI)

    Science.gov (United States)

    Warsito, Budi; Yasin, Hasbi; Ispriyanti, Dwi; Hoyyi, Abdul

    2018-05-01

    The Geographically Weighted Regression (GWR) model has been widely applied to many practical fields for exploring spatial heterogenity of a regression model. However, this method is inherently not robust to outliers. Outliers commonly exist in data sets and may lead to a distorted estimate of the underlying regression model. One of solution to handle the outliers in the regression model is to use the robust models. So this model was called Robust Geographically Weighted Regression (RGWR). This research aims to aid the government in the policy making process related to air pollution mitigation by developing a standard index model for air polluter (Air Polluter Standard Index - APSI) based on the RGWR approach. In this research, we also consider seven variables that are directly related to the air pollution level, which are the traffic velocity, the population density, the business center aspect, the air humidity, the wind velocity, the air temperature, and the area size of the urban forest. The best model is determined by the smallest AIC value. There are significance differences between Regression and RGWR in this case, but Basic GWR using the Gaussian kernel is the best model to modeling APSI because it has smallest AIC.

  16. Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions.

    Science.gov (United States)

    Rativa, Diego; Fernandes, Bruno J T; Roque, Alexandre

    2018-01-01

    Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care.

  17. Geographically weighted regression as a generalized Wombling to detect barriers to gene flow.

    Science.gov (United States)

    Diniz-Filho, José Alexandre Felizola; Soares, Thannya Nascimento; de Campos Telles, Mariana Pires

    2016-08-01

    Barriers to gene flow play an important role in structuring populations, especially in human-modified landscapes, and several methods have been proposed to detect such barriers. However, most applications of these methods require a relative large number of individuals or populations distributed in space, connected by vertices from Delaunay or Gabriel networks. Here we show, using both simulated and empirical data, a new application of geographically weighted regression (GWR) to detect such barriers, modeling the genetic variation as a "local" linear function of geographic coordinates (latitude and longitude). In the GWR, standard regression statistics, such as R(2) and slopes, are estimated for each sampling unit and thus are mapped. Peaks in these local statistics are then expected close to the barriers if genetic discontinuities exist, capturing a higher rate of population differentiation among neighboring populations. Isolation-by-Distance simulations on a longitudinally warped lattice revealed that higher local slopes from GWR coincide with the barrier detected with Monmonier algorithm. Even with a relatively small effect of the barrier, the power of local GWR in detecting the east-west barriers was higher than 95 %. We also analyzed empirical data of genetic differentiation among tree populations of Dipteryx alata and Eugenia dysenterica Brazilian Cerrado. GWR was applied to the principal coordinate of the pairwise FST matrix based on microsatellite loci. In both simulated and empirical data, the GWR results were consistent with discontinuities detected by Monmonier algorithm, as well as with previous explanations for the spatial patterns of genetic differentiation for the two species. Our analyses reveal how this new application of GWR can viewed as a generalized Wombling in a continuous space and be a useful approach to detect barriers and discontinuities to gene flow.

  18. An Efficient Local Algorithm for Distributed Multivariate Regression

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm is designed for distributed...

  19. Intelligent Quality Prediction Using Weighted Least Square Support Vector Regression

    Science.gov (United States)

    Yu, Yaojun

    A novel quality prediction method with mobile time window is proposed for small-batch producing process based on weighted least squares support vector regression (LS-SVR). The design steps and learning algorithm are also addressed. In the method, weighted LS-SVR is taken as the intelligent kernel, with which the small-batch learning is solved well and the nearer sample is set a larger weight, while the farther is set the smaller weight in the history data. A typical machining process of cutting bearing outer race is carried out and the real measured data are used to contrast experiment. The experimental results demonstrate that the prediction accuracy of the weighted LS-SVR based model is only 20%-30% that of the standard LS-SVR based one in the same condition. It provides a better candidate for quality prediction of small-batch producing process.

  20. A Scalable Local Algorithm for Distributed Multivariate Regression

    Data.gov (United States)

    National Aeronautics and Space Administration — This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm can be used for distributed...

  1. Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

    Science.gov (United States)

    Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

    2017-06-01

    A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

  2. A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction

    International Nuclear Information System (INIS)

    Yu, Jie; Chen, Kuilin; Mori, Junichi; Rashid, Mudassir M.

    2013-01-01

    Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction. - Highlights: • A novel predictive modeling method is proposed for long-term wind speed forecasting. • Gaussian mixture copula model is estimated to characterize the multi-seasonality. • Localized Gaussian process regression models can deal with the random uncertainty. • Multiple GPR models are integrated through Bayesian inference strategy. • The proposed approach shows higher prediction accuracy and reliability

  3. Estimating Loess Plateau Average Annual Precipitation with Multiple Linear Regression Kriging and Geographically Weighted Regression Kriging

    Directory of Open Access Journals (Sweden)

    Qiutong Jin

    2016-06-01

    Full Text Available Estimating the spatial distribution of precipitation is an important and challenging task in hydrology, climatology, ecology, and environmental science. In order to generate a highly accurate distribution map of average annual precipitation for the Loess Plateau in China, multiple linear regression Kriging (MLRK and geographically weighted regression Kriging (GWRK methods were employed using precipitation data from the period 1980–2010 from 435 meteorological stations. The predictors in regression Kriging were selected by stepwise regression analysis from many auxiliary environmental factors, such as elevation (DEM, normalized difference vegetation index (NDVI, solar radiation, slope, and aspect. All predictor distribution maps had a 500 m spatial resolution. Validation precipitation data from 130 hydrometeorological stations were used to assess the prediction accuracies of the MLRK and GWRK approaches. Results showed that both prediction maps with a 500 m spatial resolution interpolated by MLRK and GWRK had a high accuracy and captured detailed spatial distribution data; however, MLRK produced a lower prediction error and a higher variance explanation than GWRK, although the differences were small, in contrast to conclusions from similar studies.

  4. Development of planning level transportation safety tools using Geographically Weighted Poisson Regression.

    Science.gov (United States)

    Hadayeghi, Alireza; Shalaby, Amer S; Persaud, Bhagwant N

    2010-03-01

    A common technique used for the calibration of collision prediction models is the Generalized Linear Modeling (GLM) procedure with the assumption of Negative Binomial or Poisson error distribution. In this technique, fixed coefficients that represent the average relationship between the dependent variable and each explanatory variable are estimated. However, the stationary relationship assumed may hide some important spatial factors of the number of collisions at a particular traffic analysis zone. Consequently, the accuracy of such models for explaining the relationship between the dependent variable and the explanatory variables may be suspected since collision frequency is likely influenced by many spatially defined factors such as land use, demographic characteristics, and traffic volume patterns. The primary objective of this study is to investigate the spatial variations in the relationship between the number of zonal collisions and potential transportation planning predictors, using the Geographically Weighted Poisson Regression modeling technique. The secondary objective is to build on knowledge comparing the accuracy of Geographically Weighted Poisson Regression models to that of Generalized Linear Models. The results show that the Geographically Weighted Poisson Regression models are useful for capturing spatially dependent relationships and generally perform better than the conventional Generalized Linear Models. Copyright 2009 Elsevier Ltd. All rights reserved.

  5. A robust regression based on weighted LSSVM and penalized trimmed squares

    International Nuclear Information System (INIS)

    Liu, Jianyong; Wang, Yong; Fu, Chengqun; Guo, Jie; Yu, Qin

    2016-01-01

    Least squares support vector machine (LS-SVM) for nonlinear regression is sensitive to outliers in the field of machine learning. Weighted LS-SVM (WLS-SVM) overcomes this drawback by adding weight to each training sample. However, as the number of outliers increases, the accuracy of WLS-SVM may decrease. In order to improve the robustness of WLS-SVM, a new robust regression method based on WLS-SVM and penalized trimmed squares (WLSSVM–PTS) has been proposed. The algorithm comprises three main stages. The initial parameters are obtained by least trimmed squares at first. Then, the significant outliers are identified and eliminated by the Fast-PTS algorithm. The remaining samples with little outliers are estimated by WLS-SVM at last. The statistical tests of experimental results carried out on numerical datasets and real-world datasets show that the proposed WLSSVM–PTS is significantly robust than LS-SVM, WLS-SVM and LSSVM–LTS.

  6. Depth-weighted robust multivariate regression with application to sparse data

    KAUST Repository

    Dutta, Subhajit; Genton, Marc G.

    2017-01-01

    A robust method for multivariate regression is developed based on robust estimators of the joint location and scatter matrix of the explanatory and response variables using the notion of data depth. The multivariate regression estimator possesses desirable affine equivariance properties, achieves the best breakdown point of any affine equivariant estimator, and has an influence function which is bounded in both the response as well as the predictor variable. To increase the efficiency of this estimator, a re-weighted estimator based on robust Mahalanobis distances of the residual vectors is proposed. In practice, the method is more stable than existing methods that are constructed using subsamples of the data. The resulting multivariate regression technique is computationally feasible, and turns out to perform better than several popular robust multivariate regression methods when applied to various simulated data as well as a real benchmark data set. When the data dimension is quite high compared to the sample size it is still possible to use meaningful notions of data depth along with the corresponding depth values to construct a robust estimator in a sparse setting.

  7. Depth-weighted robust multivariate regression with application to sparse data

    KAUST Repository

    Dutta, Subhajit

    2017-04-05

    A robust method for multivariate regression is developed based on robust estimators of the joint location and scatter matrix of the explanatory and response variables using the notion of data depth. The multivariate regression estimator possesses desirable affine equivariance properties, achieves the best breakdown point of any affine equivariant estimator, and has an influence function which is bounded in both the response as well as the predictor variable. To increase the efficiency of this estimator, a re-weighted estimator based on robust Mahalanobis distances of the residual vectors is proposed. In practice, the method is more stable than existing methods that are constructed using subsamples of the data. The resulting multivariate regression technique is computationally feasible, and turns out to perform better than several popular robust multivariate regression methods when applied to various simulated data as well as a real benchmark data set. When the data dimension is quite high compared to the sample size it is still possible to use meaningful notions of data depth along with the corresponding depth values to construct a robust estimator in a sparse setting.

  8. Conjugation weights and weighted convolution algebras on totally disconnected, locally compact groups

    OpenAIRE

    Willis, George

    2013-01-01

    A family of equivalent submultiplicative weights on the to- tally disconnected, locally compact group $G$ is defined in terms of the conjugation action of $G$ on itself. These weights therefore reflect the structure of $G$, and the corresponding weighted convolution algebra is intrinsic to $G$ in the same way that $L^1(G) is.

  9. Focused information criterion and model averaging based on weighted composite quantile regression

    KAUST Repository

    Xu, Ganggang; Wang, Suojin; Huang, Jianhua Z.

    2013-01-01

    We study the focused information criterion and frequentist model averaging and their application to post-model-selection inference for weighted composite quantile regression (WCQR) in the context of the additive partial linear models. With the non

  10. Testing the water-energy theory on American palms (Arecaceae using geographically weighted regression.

    Directory of Open Access Journals (Sweden)

    Wolf L Eiserhardt

    Full Text Available Water and energy have emerged as the best contemporary environmental correlates of broad-scale species richness patterns. A corollary hypothesis of water-energy dynamics theory is that the influence of water decreases and the influence of energy increases with absolute latitude. We report the first use of geographically weighted regression for testing this hypothesis on a continuous species richness gradient that is entirely located within the tropics and subtropics. The dataset was divided into northern and southern hemispheric portions to test whether predictor shifts are more pronounced in the less oceanic northern hemisphere. American palms (Arecaceae, n = 547 spp., whose species richness and distributions are known to respond strongly to water and energy, were used as a model group. The ability of water and energy to explain palm species richness was quantified locally at different spatial scales and regressed on latitude. Clear latitudinal trends in agreement with water-energy dynamics theory were found, but the results did not differ qualitatively between hemispheres. Strong inherent spatial autocorrelation in local modeling results and collinearity of water and energy variables were identified as important methodological challenges. We overcame these problems by using simultaneous autoregressive models and variation partitioning. Our results show that the ability of water and energy to explain species richness changes not only across large climatic gradients spanning tropical to temperate or arctic zones but also within megathermal climates, at least for strictly tropical taxa such as palms. This finding suggests that the predictor shifts are related to gradual latitudinal changes in ambient energy (related to solar flux input rather than to abrupt transitions at specific latitudes, such as the occurrence of frost.

  11. Regression and Sparse Regression Methods for Viscosity Estimation of Acid Milk From it’s Sls Features

    DEFF Research Database (Denmark)

    Sharifzadeh, Sara; Skytte, Jacob Lercke; Nielsen, Otto Højager Attermann

    2012-01-01

    Statistical solutions find wide spread use in food and medicine quality control. We investigate the effect of different regression and sparse regression methods for a viscosity estimation problem using the spectro-temporal features from new Sub-Surface Laser Scattering (SLS) vision system. From...... with sparse LAR, lasso and Elastic Net (EN) sparse regression methods. Due to the inconsistent measurement condition, Locally Weighted Scatter plot Smoothing (Loess) has been employed to alleviate the undesired variation in the estimated viscosity. The experimental results of applying different methods show...

  12. Influence diagnostics in meta-regression model.

    Science.gov (United States)

    Shi, Lei; Zuo, ShanShan; Yu, Dalei; Zhou, Xiaohua

    2017-09-01

    This paper studies the influence diagnostics in meta-regression model including case deletion diagnostic and local influence analysis. We derive the subset deletion formulae for the estimation of regression coefficient and heterogeneity variance and obtain the corresponding influence measures. The DerSimonian and Laird estimation and maximum likelihood estimation methods in meta-regression are considered, respectively, to derive the results. Internal and external residual and leverage measure are defined. The local influence analysis based on case-weights perturbation scheme, responses perturbation scheme, covariate perturbation scheme, and within-variance perturbation scheme are explored. We introduce a method by simultaneous perturbing responses, covariate, and within-variance to obtain the local influence measure, which has an advantage of capable to compare the influence magnitude of influential studies from different perturbations. An example is used to illustrate the proposed methodology. Copyright © 2017 John Wiley & Sons, Ltd.

  13. Geographically Weighted Logistic Regression Applied to Credit Scoring Models

    Directory of Open Access Journals (Sweden)

    Pedro Henrique Melo Albuquerque

    Full Text Available Abstract This study used real data from a Brazilian financial institution on transactions involving Consumer Direct Credit (CDC, granted to clients residing in the Distrito Federal (DF, to construct credit scoring models via Logistic Regression and Geographically Weighted Logistic Regression (GWLR techniques. The aims were: to verify whether the factors that influence credit risk differ according to the borrower’s geographic location; to compare the set of models estimated via GWLR with the global model estimated via Logistic Regression, in terms of predictive power and financial losses for the institution; and to verify the viability of using the GWLR technique to develop credit scoring models. The metrics used to compare the models developed via the two techniques were the AICc informational criterion, the accuracy of the models, the percentage of false positives, the sum of the value of false positive debt, and the expected monetary value of portfolio default compared with the monetary value of defaults observed. The models estimated for each region in the DF were distinct in their variables and coefficients (parameters, with it being concluded that credit risk was influenced differently in each region in the study. The Logistic Regression and GWLR methodologies presented very close results, in terms of predictive power and financial losses for the institution, and the study demonstrated viability in using the GWLR technique to develop credit scoring models for the target population in the study.

  14. Tracking time-varying parameters with local regression

    DEFF Research Database (Denmark)

    Joensen, Alfred Karsten; Nielsen, Henrik Aalborg; Nielsen, Torben Skov

    2000-01-01

    This paper shows that the recursive least-squares (RLS) algorithm with forgetting factor is a special case of a varying-coe\\$cient model, and a model which can easily be estimated via simple local regression. This observation allows us to formulate a new method which retains the RLS algorithm, bu......, but extends the algorithm by including polynomial approximations. Simulation results are provided, which indicates that this new method is superior to the classical RLS method, if the parameter variations are smooth....

  15. Genetic analysis of body weights of individually fed beef bulls in South Africa using random regression models.

    Science.gov (United States)

    Selapa, N W; Nephawe, K A; Maiwashe, A; Norris, D

    2012-02-08

    The aim of this study was to estimate genetic parameters for body weights of individually fed beef bulls measured at centralized testing stations in South Africa using random regression models. Weekly body weights of Bonsmara bulls (N = 2919) tested between 1999 and 2003 were available for the analyses. The model included a fixed regression of the body weights on fourth-order orthogonal Legendre polynomials of the actual days on test (7, 14, 21, 28, 35, 42, 49, 56, 63, 70, 77, and 84) for starting age and contemporary group effects. Random regressions on fourth-order orthogonal Legendre polynomials of the actual days on test were included for additive genetic effects and additional uncorrelated random effects of the weaning-herd-year and the permanent environment of the animal. Residual effects were assumed to be independently distributed with heterogeneous variance for each test day. Variance ratios for additive genetic, permanent environment and weaning-herd-year for weekly body weights at different test days ranged from 0.26 to 0.29, 0.37 to 0.44 and 0.26 to 0.34, respectively. The weaning-herd-year was found to have a significant effect on the variation of body weights of bulls despite a 28-day adjustment period. Genetic correlations amongst body weights at different test days were high, ranging from 0.89 to 1.00. Heritability estimates were comparable to literature using multivariate models. Therefore, random regression model could be applied in the genetic evaluation of body weight of individually fed beef bulls in South Africa.

  16. Considering a non-polynomial basis for local kernel regression problem

    Science.gov (United States)

    Silalahi, Divo Dharma; Midi, Habshah

    2017-01-01

    A common used as solution for local kernel nonparametric regression problem is given using polynomial regression. In this study, we demonstrated the estimator and properties using maximum likelihood estimator for a non-polynomial basis such B-spline to replacing the polynomial basis. This estimator allows for flexibility in the selection of a bandwidth and a knot. The best estimator was selected by finding an optimal bandwidth and knot through minimizing the famous generalized validation function.

  17. Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting

    Directory of Open Access Journals (Sweden)

    C. Wu

    2018-03-01

    Full Text Available Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS, Deming regression (DR, orthogonal distance regression (ODR, weighted ODR (WODR, and York regression (YR. We first introduce a new data generation scheme that employs the Mersenne twister (MT pseudorandom number generator. The numerical simulations are also improved by (a refining the parameterization of nonlinear measurement uncertainties, (b inclusion of a linear measurement uncertainty, and (c inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot was developed to facilitate the implementation of error-in-variables regressions.

  18. Evaluation of linear regression techniques for atmospheric applications: the importance of appropriate weighting

    Science.gov (United States)

    Wu, Cheng; Zhen Yu, Jian

    2018-03-01

    Linear regression techniques are widely used in atmospheric science, but they are often improperly applied due to lack of consideration or inappropriate handling of measurement uncertainty. In this work, numerical experiments are performed to evaluate the performance of five linear regression techniques, significantly extending previous works by Chu and Saylor. The five techniques are ordinary least squares (OLS), Deming regression (DR), orthogonal distance regression (ODR), weighted ODR (WODR), and York regression (YR). We first introduce a new data generation scheme that employs the Mersenne twister (MT) pseudorandom number generator. The numerical simulations are also improved by (a) refining the parameterization of nonlinear measurement uncertainties, (b) inclusion of a linear measurement uncertainty, and (c) inclusion of WODR for comparison. Results show that DR, WODR and YR produce an accurate slope, but the intercept by WODR and YR is overestimated and the degree of bias is more pronounced with a low R2 XY dataset. The importance of a properly weighting parameter λ in DR is investigated by sensitivity tests, and it is found that an improper λ in DR can lead to a bias in both the slope and intercept estimation. Because the λ calculation depends on the actual form of the measurement error, it is essential to determine the exact form of measurement error in the XY data during the measurement stage. If a priori error in one of the variables is unknown, or the measurement error described cannot be trusted, DR, WODR and YR can provide the least biases in slope and intercept among all tested regression techniques. For these reasons, DR, WODR and YR are recommended for atmospheric studies when both X and Y data have measurement errors. An Igor Pro-based program (Scatter Plot) was developed to facilitate the implementation of error-in-variables regressions.

  19. Local regression type methods applied to the study of geophysics and high frequency financial data

    Science.gov (United States)

    Mariani, M. C.; Basu, K.

    2014-09-01

    In this work we applied locally weighted scatterplot smoothing techniques (Lowess/Loess) to Geophysical and high frequency financial data. We first analyze and apply this technique to the California earthquake geological data. A spatial analysis was performed to show that the estimation of the earthquake magnitude at a fixed location is very accurate up to the relative error of 0.01%. We also applied the same method to a high frequency data set arising in the financial sector and obtained similar satisfactory results. The application of this approach to the two different data sets demonstrates that the overall method is accurate and efficient, and the Lowess approach is much more desirable than the Loess method. The previous works studied the time series analysis; in this paper our local regression models perform a spatial analysis for the geophysics data providing different information. For the high frequency data, our models estimate the curve of best fit where data are dependent on time.

  20. Geographically weighted regression based methods for merging satellite and gauge precipitation

    Science.gov (United States)

    Chao, Lijun; Zhang, Ke; Li, Zhijia; Zhu, Yuelong; Wang, Jingfeng; Yu, Zhongbo

    2018-03-01

    Real-time precipitation data with high spatiotemporal resolutions are crucial for accurate hydrological forecasting. To improve the spatial resolution and quality of satellite precipitation, a three-step satellite and gauge precipitation merging method was formulated in this study: (1) bilinear interpolation is first applied to downscale coarser satellite precipitation to a finer resolution (PS); (2) the (mixed) geographically weighted regression methods coupled with a weighting function are then used to estimate biases of PS as functions of gauge observations (PO) and PS; and (3) biases of PS are finally corrected to produce a merged precipitation product. Based on the above framework, eight algorithms, a combination of two geographically weighted regression methods and four weighting functions, are developed to merge CMORPH (CPC MORPHing technique) precipitation with station observations on a daily scale in the Ziwuhe Basin of China. The geographical variables (elevation, slope, aspect, surface roughness, and distance to the coastline) and a meteorological variable (wind speed) were used for merging precipitation to avoid the artificial spatial autocorrelation resulting from traditional interpolation methods. The results show that the combination of the MGWR and BI-square function (MGWR-BI) has the best performance (R = 0.863 and RMSE = 7.273 mm/day) among the eight algorithms. The MGWR-BI algorithm was then applied to produce hourly merged precipitation product. Compared to the original CMORPH product (R = 0.208 and RMSE = 1.208 mm/hr), the quality of the merged data is significantly higher (R = 0.724 and RMSE = 0.706 mm/hr). The developed merging method not only improves the spatial resolution and quality of the satellite product but also is easy to implement, which is valuable for hydrological modeling and other applications.

  1. Estimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection.

    OpenAIRE

    Kim, Sanghong; Kano, Manabu; Nakagawa, Hiroshi; Hasebe, Shinji

    2011-01-01

    Development of quality estimation models using near infrared spectroscopy (NIRS) and multivariate analysis has been accelerated as a process analytical technology (PAT) tool in the pharmaceutical industry. Although linear regression methods such as partial least squares (PLS) are widely used, they cannot always achieve high estimation accuracy because physical and chemical properties of a measuring object have a complex effect on NIR spectra. In this research, locally weighted PLS (LW-PLS) wh...

  2. A Local Weighted Nearest Neighbor Algorithm and a Weighted and Constrained Least-Squared Method for Mixed Odor Analysis by Electronic Nose Systems

    Directory of Open Access Journals (Sweden)

    Jyuo-Min Shyu

    2010-11-01

    Full Text Available A great deal of work has been done to develop techniques for odor analysis by electronic nose systems. These analyses mostly focus on identifying a particular odor by comparing with a known odor dataset. However, in many situations, it would be more practical if each individual odorant could be determined directly. This paper proposes two methods for such odor components analysis for electronic nose systems. First, a K-nearest neighbor (KNN-based local weighted nearest neighbor (LWNN algorithm is proposed to determine the components of an odor. According to the component analysis, the odor training data is firstly categorized into several groups, each of which is represented by its centroid. The examined odor is then classified as the class of the nearest centroid. The distance between the examined odor and the centroid is calculated based on a weighting scheme, which captures the local structure of each predefined group. To further determine the concentration of each component, odor models are built by regressions. Then, a weighted and constrained least-squares (WCLS method is proposed to estimate the component concentrations. Experiments were carried out to assess the effectiveness of the proposed methods. The LWNN algorithm is able to classify mixed odors with different mixing ratios, while the WCLS method can provide good estimates on component concentrations.

  3. Comparison of height-diameter models based on geographically weighted regressions and linear mixed modelling applied to large scale forest inventory data

    Energy Technology Data Exchange (ETDEWEB)

    Quirós Segovia, M.; Condés Ruiz, S.; Drápela, K.

    2016-07-01

    Aim of the study: The main objective of this study was to test Geographically Weighted Regression (GWR) for developing height-diameter curves for forests on a large scale and to compare it with Linear Mixed Models (LMM). Area of study: Monospecific stands of Pinus halepensis Mill. located in the region of Murcia (Southeast Spain). Materials and Methods: The dataset consisted of 230 sample plots (2582 trees) from the Third Spanish National Forest Inventory (SNFI) randomly split into training data (152 plots) and validation data (78 plots). Two different methodologies were used for modelling local (Petterson) and generalized height-diameter relationships (Cañadas I): GWR, with different bandwidths, and linear mixed models. Finally, the quality of the estimated models was compared throughout statistical analysis. Main results: In general, both LMM and GWR provide better prediction capability when applied to a generalized height-diameter function than when applied to a local one, with R2 values increasing from around 0.6 to 0.7 in the model validation. Bias and RMSE were also lower for the generalized function. However, error analysis showed that there were no large differences between these two methodologies, evidencing that GWR provides results which are as good as the more frequently used LMM methodology, at least when no additional measurements are available for calibrating. Research highlights: GWR is a type of spatial analysis for exploring spatially heterogeneous processes. GWR can model spatial variation in tree height-diameter relationship and its regression quality is comparable to LMM. The advantage of GWR over LMM is the possibility to determine the spatial location of every parameter without additional measurements. Abbreviations: GWR (Geographically Weighted Regression); LMM (Linear Mixed Model); SNFI (Spanish National Forest Inventory). (Author)

  4. Estimation of Ordinary Differential Equation Parameters Using Constrained Local Polynomial Regression.

    Science.gov (United States)

    Ding, A Adam; Wu, Hulin

    2014-10-01

    We propose a new method to use a constrained local polynomial regression to estimate the unknown parameters in ordinary differential equation models with a goal of improving the smoothing-based two-stage pseudo-least squares estimate. The equation constraints are derived from the differential equation model and are incorporated into the local polynomial regression in order to estimate the unknown parameters in the differential equation model. We also derive the asymptotic bias and variance of the proposed estimator. Our simulation studies show that our new estimator is clearly better than the pseudo-least squares estimator in estimation accuracy with a small price of computational cost. An application example on immune cell kinetics and trafficking for influenza infection further illustrates the benefits of the proposed new method.

  5. Satellite and gauge rainfall merging using geographically weighted regression

    Directory of Open Access Journals (Sweden)

    Q. Hu

    2015-05-01

    Full Text Available A residual-based rainfall merging scheme using geographically weighted regression (GWR has been proposed. This method is capable of simultaneously blending various satellite rainfall data with gauge measurements and could describe the non-stationary influences of geographical and terrain factors on rainfall spatial distribution. Using this new method, an experimental study on merging daily rainfall from the Climate Prediction Center Morphing dataset (CMOROH and gauge measurements was conducted for the Ganjiang River basin, in Southeast China. We investigated the capability of the merging scheme for daily rainfall estimation under different gauge density. Results showed that under the condition of sparse gauge density the merging rainfall scheme is remarkably superior to the interpolation using just gauge data.

  6. Hourly cooling load forecasting using time-indexed ARX models with two-stage weighted least squares regression

    International Nuclear Information System (INIS)

    Guo, Yin; Nazarian, Ehsan; Ko, Jeonghan; Rajurkar, Kamlakar

    2014-01-01

    Highlights: • Developed hourly-indexed ARX models for robust cooling-load forecasting. • Proposed a two-stage weighted least-squares regression approach. • Considered the effect of outliers as well as trend of cooling load and weather patterns. • Included higher order terms and day type patterns in the forecasting models. • Demonstrated better accuracy compared with some ARX and ANN models. - Abstract: This paper presents a robust hourly cooling-load forecasting method based on time-indexed autoregressive with exogenous inputs (ARX) models, in which the coefficients are estimated through a two-stage weighted least squares regression. The prediction method includes a combination of two separate time-indexed ARX models to improve prediction accuracy of the cooling load over different forecasting periods. The two-stage weighted least-squares regression approach in this study is robust to outliers and suitable for fast and adaptive coefficient estimation. The proposed method is tested on a large-scale central cooling system in an academic institution. The numerical case studies show the proposed prediction method performs better than some ANN and ARX forecasting models for the given test data set

  7. Regression and local control rates after radiotherapy for jugulotympanic paragangliomas: Systematic review and meta-analysis

    International Nuclear Information System (INIS)

    Hulsteijn, Leonie T. van; Corssmit, Eleonora P.M.; Coremans, Ida E.M.; Smit, Johannes W.A.; Jansen, Jeroen C.; Dekkers, Olaf M.

    2013-01-01

    The primary treatment goal of radiotherapy for paragangliomas of the head and neck region (HNPGLs) is local control of the tumor, i.e. stabilization of tumor volume. Interestingly, regression of tumor volume has also been reported. Up to the present, no meta-analysis has been performed giving an overview of regression rates after radiotherapy in HNPGLs. The main objective was to perform a systematic review and meta-analysis to assess regression of tumor volume in HNPGL-patients after radiotherapy. A second outcome was local tumor control. Design of the study is systematic review and meta-analysis. PubMed, EMBASE, Web of Science, COCHRANE and Academic Search Premier and references of key articles were searched in March 2012 to identify potentially relevant studies. Considering the indolent course of HNPGLs, only studies with ⩾12 months follow-up were eligible. Main outcomes were the pooled proportions of regression and local control after radiotherapy as initial, combined (i.e. directly post-operatively or post-embolization) or salvage treatment (i.e. after initial treatment has failed) for HNPGLs. A meta-analysis was performed with an exact likelihood approach using a logistic regression with a random effect at the study level. Pooled proportions with 95% confidence intervals (CI) were reported. Fifteen studies were included, concerning a total of 283 jugulotympanic HNPGLs in 276 patients. Pooled regression proportions for initial, combined and salvage treatment were respectively 21%, 33% and 52% in radiosurgery studies and 4%, 0% and 64% in external beam radiotherapy studies. Pooled local control proportions for radiotherapy as initial, combined and salvage treatment ranged from 79% to 100%. Radiotherapy for jugulotympanic paragangliomas results in excellent local tumor control and therefore is a valuable treatment for these types of tumors. The effects of radiotherapy on regression of tumor volume remain ambiguous, although the data suggest that regression can

  8. A Proportional Hazards Regression Model for the Subdistribution with Covariates-adjusted Censoring Weight for Competing Risks Data

    DEFF Research Database (Denmark)

    He, Peng; Eriksson, Frank; Scheike, Thomas H.

    2016-01-01

    function by fitting the Cox model for the censoring distribution and using the predictive probability for each individual. Our simulation study shows that the covariate-adjusted weight estimator is basically unbiased when the censoring time depends on the covariates, and the covariate-adjusted weight......With competing risks data, one often needs to assess the treatment and covariate effects on the cumulative incidence function. Fine and Gray proposed a proportional hazards regression model for the subdistribution of a competing risk with the assumption that the censoring distribution...... and the covariates are independent. Covariate-dependent censoring sometimes occurs in medical studies. In this paper, we study the proportional hazards regression model for the subdistribution of a competing risk with proper adjustments for covariate-dependent censoring. We consider a covariate-adjusted weight...

  9. The Crash Intensity Evaluation Using General Centrality Criterions and a Geographically Weighted Regression

    Science.gov (United States)

    Ghadiriyan Arani, M.; Pahlavani, P.; Effati, M.; Noori Alamooti, F.

    2017-09-01

    Today, one of the social problems influencing on the lives of many people is the road traffic crashes especially the highway ones. In this regard, this paper focuses on highway of capital and the most populous city in the U.S. state of Georgia and the ninth largest metropolitan area in the United States namely Atlanta. Geographically weighted regression and general centrality criteria are the aspects of traffic used for this article. In the first step, in order to estimate of crash intensity, it is needed to extract the dual graph from the status of streets and highways to use general centrality criteria. With the help of the graph produced, the criteria are: Degree, Pageranks, Random walk, Eccentricity, Closeness, Betweenness, Clustering coefficient, Eigenvector, and Straightness. The intensity of crash point is counted for every highway by dividing the number of crashes in that highway to the total number of crashes. Intensity of crash point is calculated for each highway. Then, criteria and crash point were normalized and the correlation between them was calculated to determine the criteria that are not dependent on each other. The proposed hybrid approach is a good way to regression issues because these effective measures result to a more desirable output. R2 values for geographically weighted regression using the Gaussian kernel was 0.539 and also 0.684 was obtained using a triple-core cube. The results showed that the triple-core cube kernel is better for modeling the crash intensity.

  10. Geographically Weighted Regression Model with Kernel Bisquare and Tricube Weighted Function on Poverty Percentage Data in Central Java Province

    Science.gov (United States)

    Nugroho, N. F. T. A.; Slamet, I.

    2018-05-01

    Poverty is a socio-economic condition of a person or group of people who can not fulfil their basic need to maintain and develop a dignified life. This problem still cannot be solved completely in Central Java Province. Currently, the percentage of poverty in Central Java is 13.32% which is higher than the national poverty rate which is 11.13%. In this research, data of percentage of poor people in Central Java Province has been analyzed through geographically weighted regression (GWR). The aim of this research is therefore to model poverty percentage data in Central Java Province using GWR with weighted function of kernel bisquare, and tricube. As the results, we obtained GWR model with bisquare and tricube kernel weighted function on poverty percentage data in Central Java province. From the GWR model, there are three categories of region which are influenced by different of significance factors.

  11. Gaussian process regression for sensor networks under localization uncertainty

    Science.gov (United States)

    Jadaliha, M.; Xu, Yunfei; Choi, Jongeun; Johnson, N.S.; Li, Weiming

    2013-01-01

    In this paper, we formulate Gaussian process regression with observations under the localization uncertainty due to the resource-constrained sensor networks. In our formulation, effects of observations, measurement noise, localization uncertainty, and prior distributions are all correctly incorporated in the posterior predictive statistics. The analytically intractable posterior predictive statistics are proposed to be approximated by two techniques, viz., Monte Carlo sampling and Laplace's method. Such approximation techniques have been carefully tailored to our problems and their approximation error and complexity are analyzed. Simulation study demonstrates that the proposed approaches perform much better than approaches without considering the localization uncertainty properly. Finally, we have applied the proposed approaches on the experimentally collected real data from a dye concentration field over a section of a river and a temperature field of an outdoor swimming pool to provide proof of concept tests and evaluate the proposed schemes in real situations. In both simulation and experimental results, the proposed methods outperform the quick-and-dirty solutions often used in practice.

  12. Combining Alphas via Bounded Regression

    Directory of Open Access Journals (Sweden)

    Zura Kakushadze

    2015-11-01

    Full Text Available We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications, typically, there is insufficient history to compute a sample covariance matrix (SCM for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.

  13. Weighted linear regression using D2H and D2 as the independent variables

    Science.gov (United States)

    Hans T. Schreuder; Michael S. Williams

    1998-01-01

    Several error structures for weighted regression equations used for predicting volume were examined for 2 large data sets of felled and standing loblolly pine trees (Pinus taeda L.). The generally accepted model with variance of error proportional to the value of the covariate squared ( D2H = diameter squared times height or D...

  14. THE CRASH INTENSITY EVALUATION USING GENERAL CENTRALITY CRITERIONS AND A GEOGRAPHICALLY WEIGHTED REGRESSION

    Directory of Open Access Journals (Sweden)

    M. Ghadiriyan Arani

    2017-09-01

    Full Text Available Today, one of the social problems influencing on the lives of many people is the road traffic crashes especially the highway ones. In this regard, this paper focuses on highway of capital and the most populous city in the U.S. state of Georgia and the ninth largest metropolitan area in the United States namely Atlanta. Geographically weighted regression and general centrality criteria are the aspects of traffic used for this article. In the first step, in order to estimate of crash intensity, it is needed to extract the dual graph from the status of streets and highways to use general centrality criteria. With the help of the graph produced, the criteria are: Degree, Pageranks, Random walk, Eccentricity, Closeness, Betweenness, Clustering coefficient, Eigenvector, and Straightness. The intensity of crash point is counted for every highway by dividing the number of crashes in that highway to the total number of crashes. Intensity of crash point is calculated for each highway. Then, criteria and crash point were normalized and the correlation between them was calculated to determine the criteria that are not dependent on each other. The proposed hybrid approach is a good way to regression issues because these effective measures result to a more desirable output. R2 values for geographically weighted regression using the Gaussian kernel was 0.539 and also 0.684 was obtained using a triple-core cube. The results showed that the triple-core cube kernel is better for modeling the crash intensity.

  15. Issues in weighting bioassay data for use in regressions for internal dose assessments

    International Nuclear Information System (INIS)

    Strom, D.J.

    1992-11-01

    For use of bioassay data in internal dose assessment, research should be done to clarify the goal desired, the choice of method to achieve the goal, the selection of adjustable parameters, and on the ensemble of information that is available. Understanding of these issues should determine choices of weighting factors for bioassay data used in regression models. This paper provides an assessment of the relative importance of the various factors

  16. The Effect of a Sports Stadium on Housing Rents: An Application of Geographically Weighted Regression

    Directory of Open Access Journals (Sweden)

    Jorge Enrique Agudelo Torres

    2015-06-01

    Full Text Available Researchers have determined that real estate prices vary in continuous ways as a function of spatial characteristics.  In this study we examine whether geographically weighted regression (GWR provides different estimates of price effects around a sports stadium than more traditional regression techniques.  We find that an application of GWR with hedonic prices finds that the stadium has a negative external effect on housing rents that extends outward 560 meters, in contrast to the positive external effect on housing rents found using a conventional estimation technique.

  17. Multivariate Local Polynomial Regression with Application to Shenzhen Component Index

    Directory of Open Access Journals (Sweden)

    Liyun Su

    2011-01-01

    Full Text Available This study attempts to characterize and predict stock index series in Shenzhen stock market using the concepts of multivariate local polynomial regression. Based on nonlinearity and chaos of the stock index time series, multivariate local polynomial prediction methods and univariate local polynomial prediction method, all of which use the concept of phase space reconstruction according to Takens' Theorem, are considered. To fit the stock index series, the single series changes into bivariate series. To evaluate the results, the multivariate predictor for bivariate time series based on multivariate local polynomial model is compared with univariate predictor with the same Shenzhen stock index data. The numerical results obtained by Shenzhen component index show that the prediction mean squared error of the multivariate predictor is much smaller than the univariate one and is much better than the existed three methods. Even if the last half of the training data are used in the multivariate predictor, the prediction mean squared error is smaller than the univariate predictor. Multivariate local polynomial prediction model for nonsingle time series is a useful tool for stock market price prediction.

  18. Auto-associative Kernel Regression Model with Weighted Distance Metric for Instrument Drift Monitoring

    International Nuclear Information System (INIS)

    Shin, Ho Cheol; Park, Moon Ghu; You, Skin

    2006-01-01

    Recently, many on-line approaches to instrument channel surveillance (drift monitoring and fault detection) have been reported worldwide. On-line monitoring (OLM) method evaluates instrument channel performance by assessing its consistency with other plant indications through parametric or non-parametric models. The heart of an OLM system is the model giving an estimate of the true process parameter value against individual measurements. This model gives process parameter estimate calculated as a function of other plant measurements which can be used to identify small sensor drifts that would require the sensor to be manually calibrated or replaced. This paper describes an improvement of auto associative kernel regression (AAKR) by introducing a correlation coefficient weighting on kernel distances. The prediction performance of the developed method is compared with conventional auto-associative kernel regression

  19. A New Global Regression Analysis Method for the Prediction of Wind Tunnel Model Weight Corrections

    Science.gov (United States)

    Ulbrich, Norbert Manfred; Bridge, Thomas M.; Amaya, Max A.

    2014-01-01

    A new global regression analysis method is discussed that predicts wind tunnel model weight corrections for strain-gage balance loads during a wind tunnel test. The method determines corrections by combining "wind-on" model attitude measurements with least squares estimates of the model weight and center of gravity coordinates that are obtained from "wind-off" data points. The method treats the least squares fit of the model weight separate from the fit of the center of gravity coordinates. Therefore, it performs two fits of "wind- off" data points and uses the least squares estimator of the model weight as an input for the fit of the center of gravity coordinates. Explicit equations for the least squares estimators of the weight and center of gravity coordinates are derived that simplify the implementation of the method in the data system software of a wind tunnel. In addition, recommendations for sets of "wind-off" data points are made that take typical model support system constraints into account. Explicit equations of the confidence intervals on the model weight and center of gravity coordinates and two different error analyses of the model weight prediction are also discussed in the appendices of the paper.

  20. Predicting Factors of INSURE Failure in Low Birth Weight Neonates with RDS; A Logistic Regression Model

    Directory of Open Access Journals (Sweden)

    Bita Najafian

    2015-02-01

    Full Text Available Background:Respiratory Distress syndrome is the most common respiratory disease in premature neonate and the most important cause of death among them. We aimed to investigate factors to predict successful or failure of INSURE method as a therapeutic method of RDS.Methods:In a cohort study,45 neonates with diagnosed RDS and birth weight lower than 1500g were included and they underwent INSURE followed by NCPAP(Nasal Continuous Positive Airway Pressure. The patients were divided into failure or successful groups and factors which can predict success of INSURE were investigated by logistic regression in SPSS 16th version.Results:29 and16 neonates were observed in successful and failure groups, respectively. Birth weight was the only variable with significant difference between two groups (P=0.002. Finally logistic regression test showed that birth weight is only predicting factor for success (P: 0.001, EXP[β]: 0.009, CI [95%]: 1.003-0.014 and mortality (P: 0.029, EXP[β]: 0.993, CI [95%]: 0.987-0.999 of neonates treated with INSURE method.Conclusion:Predicting factors which affect on success rate of INSURE can be useful for treating and reducing charge of neonate with RDS and the birth weight is one of the effective factor on INSURE Success in this study.

  1. Predicting Factors of INSURE Failure in Low Birth Weight Neonates with RDS; A Logistic Regression Model

    Directory of Open Access Journals (Sweden)

    Bita Najafian

    2015-02-01

    Full Text Available Background:Respiratory Distress syndrome is the most common respiratory disease in premature neonate and the most important cause of death among them. We aimed to investigate factors to predict successful or failure of INSURE method as a therapeutic method of RDS. Methods:In a cohort study,45 neonates with diagnosed RDS and birth weight lower than 1500g were included and they underwent INSURE followed by NCPAP(Nasal Continuous Positive Airway Pressure. The patients were divided into failure or successful groups and factors which can predict success of INSURE were investigated by logistic regression in SPSS 16th version. Results:29 and16 neonates were observed in successful and failure groups, respectively. Birth weight was the only variable with significant difference between two groups (P=0.002. Finally logistic regression test showed that birth weight is only predicting factor for success (P: 0.001, EXP[β]: 0.009, CI [95%]: 1.003-0.014 and mortality (P: 0.029, EXP[β]: 0.993, CI [95%]: 0.987-0.999 of neonates treated with INSURE method. Conclusion:Predicting factors which affect on success rate of INSURE can be useful for treating and reducing charge of neonate with RDS and the birth weight is one of the effective factor on INSURE Success in this study.

  2. Guided SAR image despeckling with probabilistic non local weights

    Science.gov (United States)

    Gokul, Jithin; Nair, Madhu S.; Rajan, Jeny

    2017-12-01

    SAR images are generally corrupted by granular disturbances called speckle, which makes visual analysis and detail extraction a difficult task. Non Local despeckling techniques with probabilistic similarity has been a recent trend in SAR despeckling. To achieve effective speckle suppression without compromising detail preservation, we propose an improvement for the existing Generalized Guided Filter with Bayesian Non-Local Means (GGF-BNLM) method. The proposed method (Guided SAR Image Despeckling with Probabilistic Non Local Weights) replaces parametric constants based on heuristics in GGF-BNLM method with dynamically derived values based on the image statistics for weight computation. Proposed changes make GGF-BNLM method adaptive and as a result, significant improvement is achieved in terms of performance. Experimental analysis on SAR images shows excellent speckle reduction without compromising feature preservation when compared to GGF-BNLM method. Results are also compared with other state-of-the-art and classic SAR depseckling techniques to demonstrate the effectiveness of the proposed method.

  3. Estimating monotonic rates from biological data using local linear regression.

    Science.gov (United States)

    Olito, Colin; White, Craig R; Marshall, Dustin J; Barneche, Diego R

    2017-03-01

    Accessing many fundamental questions in biology begins with empirical estimation of simple monotonic rates of underlying biological processes. Across a variety of disciplines, ranging from physiology to biogeochemistry, these rates are routinely estimated from non-linear and noisy time series data using linear regression and ad hoc manual truncation of non-linearities. Here, we introduce the R package LoLinR, a flexible toolkit to implement local linear regression techniques to objectively and reproducibly estimate monotonic biological rates from non-linear time series data, and demonstrate possible applications using metabolic rate data. LoLinR provides methods to easily and reliably estimate monotonic rates from time series data in a way that is statistically robust, facilitates reproducible research and is applicable to a wide variety of research disciplines in the biological sciences. © 2017. Published by The Company of Biologists Ltd.

  4. Weighted local Hardy spaces associated with operators

    Indian Academy of Sciences (India)

    RUMING GONG

    2018-04-24

    5 days ago ... Studies 116 (1985) (Amsterdam: North Holland). [12] Gong R M and Yan L X, Littlewood–Paley and spectral multipliers on weighted L p spaces, J. Geom. Anal. 24 (2014) 873–900. [13] Gong R M, Li J and Yan L X, A local version of Hardy spaces associated with operators on metric spaces, Sci. China Math.

  5. Focused information criterion and model averaging based on weighted composite quantile regression

    KAUST Repository

    Xu, Ganggang

    2013-08-13

    We study the focused information criterion and frequentist model averaging and their application to post-model-selection inference for weighted composite quantile regression (WCQR) in the context of the additive partial linear models. With the non-parametric functions approximated by polynomial splines, we show that, under certain conditions, the asymptotic distribution of the frequentist model averaging WCQR-estimator of a focused parameter is a non-linear mixture of normal distributions. This asymptotic distribution is used to construct confidence intervals that achieve the nominal coverage probability. With properly chosen weights, the focused information criterion based WCQR estimators are not only robust to outliers and non-normal residuals but also can achieve efficiency close to the maximum likelihood estimator, without assuming the true error distribution. Simulation studies and a real data analysis are used to illustrate the effectiveness of the proposed procedure. © 2013 Board of the Foundation of the Scandinavian Journal of Statistics..

  6. Method validation using weighted linear regression models for quantification of UV filters in water samples.

    Science.gov (United States)

    da Silva, Claudia Pereira; Emídio, Elissandro Soares; de Marchi, Mary Rosa Rodrigues

    2015-01-01

    This paper describes the validation of a method consisting of solid-phase extraction followed by gas chromatography-tandem mass spectrometry for the analysis of the ultraviolet (UV) filters benzophenone-3, ethylhexyl salicylate, ethylhexyl methoxycinnamate and octocrylene. The method validation criteria included evaluation of selectivity, analytical curve, trueness, precision, limits of detection and limits of quantification. The non-weighted linear regression model has traditionally been used for calibration, but it is not necessarily the optimal model in all cases. Because the assumption of homoscedasticity was not met for the analytical data in this work, a weighted least squares linear regression was used for the calibration method. The evaluated analytical parameters were satisfactory for the analytes and showed recoveries at four fortification levels between 62% and 107%, with relative standard deviations less than 14%. The detection limits ranged from 7.6 to 24.1 ng L(-1). The proposed method was used to determine the amount of UV filters in water samples from water treatment plants in Araraquara and Jau in São Paulo, Brazil. Copyright © 2014 Elsevier B.V. All rights reserved.

  7. Analisis Faktor – Faktor yang Mempengaruhi Jumlah Kejahatan Pencurian Kendaraan Bermotor (Curanmor) Menggunakan Model Geographically Weighted Poisson Regression (Gwpr)

    OpenAIRE

    Haris, Muhammad; Yasin, Hasbi; Hoyyi, Abdul

    2015-01-01

    Theft is an act taking someone else's property, partially or entierely, with intention to have it illegally. Motor vehicle theft is one of the most highlighted crime type and disturbing the communities. Regression analysis is a statistical analysis for modeling the relationships between response variable and predictor variable. If the response variable follows a Poisson distribution or categorized as a count data, so the regression model used is Poisson regression. Geographically Weighted Poi...

  8. Estimate the contribution of incubation parameters influence egg hatchability using multiple linear regression analysis.

    Science.gov (United States)

    Khalil, Mohamed H; Shebl, Mostafa K; Kosba, Mohamed A; El-Sabrout, Karim; Zaki, Nesma

    2016-08-01

    This research was conducted to determine the most affecting parameters on hatchability of indigenous and improved local chickens' eggs. Five parameters were studied (fertility, early and late embryonic mortalities, shape index, egg weight, and egg weight loss) on four strains, namely Fayoumi, Alexandria, Matrouh, and Montazah. Multiple linear regression was performed on the studied parameters to determine the most influencing one on hatchability. The results showed significant differences in commercial and scientific hatchability among strains. Alexandria strain has the highest significant commercial hatchability (80.70%). Regarding the studied strains, highly significant differences in hatching chick weight among strains were observed. Using multiple linear regression analysis, fertility made the greatest percent contribution (71.31%) to hatchability, and the lowest percent contributions were made by shape index and egg weight loss. A prediction of hatchability using multiple regression analysis could be a good tool to improve hatchability percentage in chickens.

  9. Use of Geographically Weighted Regression (GWR Method to Estimate the Effects of Location Attributes on the Residential Property Values

    Directory of Open Access Journals (Sweden)

    Mohd Faris Dziauddin

    2017-07-01

    Full Text Available This study estimates the effect of locational attributes on residential property values in Kuala Lumpur, Malaysia. Geographically weighted regression (GWR enables the use of the local parameter rather than the global parameter to be estimated, with the results presented in map form. The results of this study reveal that residential property values are mainly determined by the property’s physical (structural attributes, but proximity to locational attributes also contributes marginally. The use of GWR in this study is considered a better approach than other methods to examine the effect of locational attributes on residential property values. GWR has the capability to produce meaningful results in which different locational attributes have differential spatial effects across a geographical area on residential property values. This method has the ability to determine the factors on which premiums depend, and in turn it can assist the government in taxation matters.

  10. Equivalent charge source model based iterative maximum neighbor weight for sparse EEG source localization.

    Science.gov (United States)

    Xu, Peng; Tian, Yin; Lei, Xu; Hu, Xiao; Yao, Dezhong

    2008-12-01

    How to localize the neural electric activities within brain effectively and precisely from the scalp electroencephalogram (EEG) recordings is a critical issue for current study in clinical neurology and cognitive neuroscience. In this paper, based on the charge source model and the iterative re-weighted strategy, proposed is a new maximum neighbor weight based iterative sparse source imaging method, termed as CMOSS (Charge source model based Maximum neighbOr weight Sparse Solution). Different from the weight used in focal underdetermined system solver (FOCUSS) where the weight for each point in the discrete solution space is independently updated in iterations, the new designed weight for each point in each iteration is determined by the source solution of the last iteration at both the point and its neighbors. Using such a new weight, the next iteration may have a bigger chance to rectify the local source location bias existed in the previous iteration solution. The simulation studies with comparison to FOCUSS and LORETA for various source configurations were conducted on a realistic 3-shell head model, and the results confirmed the validation of CMOSS for sparse EEG source localization. Finally, CMOSS was applied to localize sources elicited in a visual stimuli experiment, and the result was consistent with those source areas involved in visual processing reported in previous studies.

  11. Drusen regression is associated with local changes in fundus autofluorescence in intermediate age-related macular degeneration.

    Science.gov (United States)

    Toy, Brian C; Krishnadev, Nupura; Indaram, Maanasa; Cunningham, Denise; Cukras, Catherine A; Chew, Emily Y; Wong, Wai T

    2013-09-01

    To investigate the association of spontaneous drusen regression in intermediate age-related macular degeneration (AMD) with changes on fundus photography and fundus autofluorescence (FAF) imaging. Prospective observational case series. Fundus images from 58 eyes (in 58 patients) with intermediate AMD and large drusen were assessed over 2 years for areas of drusen regression that exceeded the area of circle C1 (diameter 125 μm; Age-Related Eye Disease Study grading protocol). Manual segmentation and computer-based image analysis were used to detect and delineate areas of drusen regression. Delineated regions were graded as to their appearance on fundus photographs and FAF images, and changes in FAF signal were graded manually and quantitated using automated image analysis. Drusen regression was detected in approximately half of study eyes using manual (48%) and computer-assisted (50%) techniques. At year-2, the clinical appearance of areas of drusen regression on fundus photography was mostly unremarkable, with a majority of eyes (71%) demonstrating no detectable clinical abnormalities, and the remainder (29%) showing minor pigmentary changes. However, drusen regression areas were associated with local changes in FAF that were significantly more prominent than changes on fundus photography. A majority of eyes (64%-66%) demonstrated a predominant decrease in overall FAF signal, while 14%-21% of eyes demonstrated a predominant increase in overall FAF signal. FAF imaging demonstrated that drusen regression in intermediate AMD was often accompanied by changes in local autofluorescence signal. Drusen regression may be associated with concurrent structural and physiologic changes in the outer retina. Published by Elsevier Inc.

  12. Particle swarm optimization-based local entropy weighted histogram equalization for infrared image enhancement

    Science.gov (United States)

    Wan, Minjie; Gu, Guohua; Qian, Weixian; Ren, Kan; Chen, Qian; Maldague, Xavier

    2018-06-01

    Infrared image enhancement plays a significant role in intelligent urban surveillance systems for smart city applications. Unlike existing methods only exaggerating the global contrast, we propose a particle swam optimization-based local entropy weighted histogram equalization which involves the enhancement of both local details and fore-and background contrast. First of all, a novel local entropy weighted histogram depicting the distribution of detail information is calculated based on a modified hyperbolic tangent function. Then, the histogram is divided into two parts via a threshold maximizing the inter-class variance in order to improve the contrasts of foreground and background, respectively. To avoid over-enhancement and noise amplification, double plateau thresholds of the presented histogram are formulated by means of particle swarm optimization algorithm. Lastly, each sub-image is equalized independently according to the constrained sub-local entropy weighted histogram. Comparative experiments implemented on real infrared images prove that our algorithm outperforms other state-of-the-art methods in terms of both visual and quantized evaluations.

  13. Poisson and Gaussian approximation of weighted local empirical processes

    NARCIS (Netherlands)

    Einmahl, J.H.J.

    1995-01-01

    We consider the local empirical process indexed by sets, a greatly generalized version of the well-studied uniform tail empirical process. We show that the weak limit of weighted versions of this process is Poisson under certain conditions, whereas it is Gaussian in other situations. Our main

  14. Source apportionment of soil heavy metals using robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR) receptor model.

    Science.gov (United States)

    Qu, Mingkai; Wang, Yan; Huang, Biao; Zhao, Yongcun

    2018-06-01

    The traditional source apportionment models, such as absolute principal component scores-multiple linear regression (APCS-MLR), are usually susceptible to outliers, which may be widely present in the regional geochemical dataset. Furthermore, the models are merely built on variable space instead of geographical space and thus cannot effectively capture the local spatial characteristics of each source contributions. To overcome the limitations, a new receptor model, robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR), was proposed based on the traditional APCS-MLR model. Then, the new method was applied to the source apportionment of soil metal elements in a region of Wuhan City, China as a case study. Evaluations revealed that: (i) RAPCS-RGWR model had better performance than APCS-MLR model in the identification of the major sources of soil metal elements, and (ii) source contributions estimated by RAPCS-RGWR model were more close to the true soil metal concentrations than that estimated by APCS-MLR model. It is shown that the proposed RAPCS-RGWR model is a more effective source apportionment method than APCS-MLR (i.e., non-robust and global model) in dealing with the regional geochemical dataset. Copyright © 2018 Elsevier B.V. All rights reserved.

  15. An age estimation method using brain local features for T1-weighted images.

    Science.gov (United States)

    Kondo, Chihiro; Ito, Koichi; Kai Wu; Sato, Kazunori; Taki, Yasuyuki; Fukuda, Hiroshi; Aoki, Takafumi

    2015-08-01

    Previous statistical analysis studies using large-scale brain magnetic resonance (MR) image databases have examined that brain tissues have age-related morphological changes. This fact indicates that one can estimate the age of a subject from his/her brain MR image by evaluating morphological changes with healthy aging. This paper proposes an age estimation method using local features extracted from T1-weighted MR images. The brain local features are defined by volumes of brain tissues parcellated into local regions defined by the automated anatomical labeling atlas. The proposed method selects optimal local regions to improve the performance of age estimation. We evaluate performance of the proposed method using 1,146 T1-weighted images from a Japanese MR image database. We also discuss the medical implication of selected optimal local regions.

  16. A nonparametric approach to calculate critical micelle concentrations: the local polynomial regression method

    Energy Technology Data Exchange (ETDEWEB)

    Lopez Fontan, J.L.; Costa, J.; Ruso, J.M.; Prieto, G. [Dept. of Applied Physics, Univ. of Santiago de Compostela, Santiago de Compostela (Spain); Sarmiento, F. [Dept. of Mathematics, Faculty of Informatics, Univ. of A Coruna, A Coruna (Spain)

    2004-02-01

    The application of a statistical method, the local polynomial regression method, (LPRM), based on a nonparametric estimation of the regression function to determine the critical micelle concentration (cmc) is presented. The method is extremely flexible because it does not impose any parametric model on the subjacent structure of the data but rather allows the data to speak for themselves. Good concordance of cmc values with those obtained by other methods was found for systems in which the variation of a measured physical property with concentration showed an abrupt change. When this variation was slow, discrepancies between the values obtained by LPRM and others methods were found. (orig.)

  17. Regression tree analysis for predicting body weight of Nigerian Muscovy duck (Cairina moschata

    Directory of Open Access Journals (Sweden)

    Oguntunji Abel Olusegun

    2017-01-01

    Full Text Available Morphometric parameters and their indices are central to the understanding of the type and function of livestock. The present study was conducted to predict body weight (BWT of adult Nigerian Muscovy ducks from nine (9 morphometric parameters and seven (7 body indices and also to identify the most important predictor of BWT among them using regression tree analysis (RTA. The experimental birds comprised of 1,020 adult male and female Nigerian Muscovy ducks randomly sampled in Rain Forest (203, Guinea Savanna (298 and Derived Savanna (519 agro-ecological zones. Result of RTA revealed that compactness; body girth and massiveness were the most important independent variables in predicting BWT and were used in constructing RT. The combined effect of the three predictors was very high and explained 91.00% of the observed variation of the target variable (BWT. The optimal regression tree suggested that Muscovy ducks with compactness >5.765 would be fleshy and have highest BWT. The result of the present study could be exploited by animal breeders and breeding companies in selection and improvement of BWT of Muscovy ducks.

  18. Length-weight regressions of the microcrustacean species from a tropical floodplain Regressões peso-comprimento das espécies de microcrustáceos em uma planície de inundação tropical

    Directory of Open Access Journals (Sweden)

    Fábio de Azevedo

    2012-03-01

    Full Text Available AIM: This study presents length-weight regressions adjusted for the most representative microcrustacean species and young stages of copepods from tropical lakes, together with a comparison of these results with estimates from the literature for tropical and temperate regions; METHODS: Samples were taken from six isolated lakes, in summer and winter, using a motorized pump and plankton net. The dry weight of each size class (for cladocerans or developmental stage (for copepods was measured using an electronic microbalance; RESULTS: Adjusted regressions were significant. We observed a trend of under-estimating the weights of smaller species and overestimating those of larger species, when using regressions obtained from temperate regions; CONCLUSION: We must be cautious about using pooled regressions from the literature, preferring models of similar species, or weighing the organisms and building new models.OBJETIVO: Este estudo apresenta as regressões peso-comprimento elaboradas para as espécies mais representativas de microcrustáceos e formas jovens de copépodes em lagos tropicais, bem como a comparação desses resultados com as estimativas da literatura para as regiões tropical e temperada; MÉTODOS: As amostragens foram realizadas em seis lagoas isoladas, no verão e no inverno, usando moto-bomba e rede de plâncton. O peso seco de cada classe de tamanho (para cladóceros e estágio de desenvolvimento (copépodes foi medido em microbalança eletrônica; RESULTADOS: As regressões ajustadas foram significativas. Observamos uma tendência em subestimar o peso das espécies de menor porte e superestimar as espécies de maior porte, quando se utiliza regressões peso-comprimento obtidas para a região de clima temperado; CONCLUSÃO: Devemos ter cautela no uso de regressões peso-comprimento existentes na literatura, preferindo modelos para as mesmas espécies, ou pesar os organismos e construir os próprios modelos.

  19. OCT despeckling via weighted nuclear norm constrained non-local low-rank representation

    Science.gov (United States)

    Tang, Chang; Zheng, Xiao; Cao, Lijuan

    2017-10-01

    As a non-invasive imaging modality, optical coherence tomography (OCT) plays an important role in medical sciences. However, OCT images are always corrupted by speckle noise, which can mask image features and pose significant challenges for medical analysis. In this work, we propose an OCT despeckling method by using non-local, low-rank representation with weighted nuclear norm constraint. Unlike previous non-local low-rank representation based OCT despeckling methods, we first generate a guidance image to improve the non-local group patches selection quality, then a low-rank optimization model with a weighted nuclear norm constraint is formulated to process the selected group patches. The corrupted probability of each pixel is also integrated into the model as a weight to regularize the representation error term. Note that each single patch might belong to several groups, hence different estimates of each patch are aggregated to obtain its final despeckled result. Both qualitative and quantitative experimental results on real OCT images show the superior performance of the proposed method compared with other state-of-the-art speckle removal techniques.

  20. PEMODELAN JUMLAH ANAK PUTUS SEKOLAH DI PROVINSI BALI DENGAN PENDEKATAN SEMI-PARAMETRIC GEOGRAPHICALLY WEIGHTED POISSON REGRESSION

    Directory of Open Access Journals (Sweden)

    GUSTI AYU RATIH ASTARI

    2013-11-01

    Full Text Available Dropout number is one of the important indicators to measure the human progress resources in education sector. This research uses the approaches of Semi-parametric Geographically Weighted Poisson Regression to get the best model and to determine the influencing factors of dropout number for primary education in Bali. The analysis results show that there are no significant differences between the Poisson regression model with GWPR and Semi-parametric GWPR. Factors which significantly influence the dropout number for primary education in Bali are the ratio of students to school, ratio of students to teachers, the number of families with the latest educational fathers is elementary or junior high school, illiteracy rates, and the average number of family members.

  1. Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings.

    Science.gov (United States)

    Choi, Jae-Seok; Kim, Munchurl

    2017-03-01

    Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between Peak-Signal-to-Noise Ratio (PSNR) performances and computational complexity. However, since SI only utilizes simple linear mappings, it may fail to precisely reconstruct HR patches with complex texture. In this paper, we present a novel SR method, which inherits the large-to-small patch conversion scheme from SI but uses global regression based on local linear mappings (GLM). Thus, our new SR method is called GLM-SI. In GLM-SI, each LR input patch is divided into 25 overlapped subpatches. Next, based on the local properties of these subpatches, 25 different local linear mappings are applied to the current LR input patch to generate 25 HR patch candidates, which are then regressed into one final HR patch using a global regressor. The local linear mappings are learned cluster-wise in our off-line training phase. The main contribution of this paper is as follows: Previously, linear-mapping-based conventional SR methods, including SI only used one simple yet coarse linear mapping to each patch to reconstruct its HR version. On the contrary, for each LR input patch, our GLM-SI is the first to apply a combination of multiple local linear mappings, where each local linear mapping is found according to local properties of the current LR patch. Therefore, it can better approximate nonlinear LR-to-HR mappings for HR patches with complex texture. Experiment results show that the proposed GLM-SI method outperforms most of the state-of-the-art methods, and shows comparable PSNR performance with much lower

  2. Analysis of the relationship between community characteristics and depression using geographically weighted regression.

    Science.gov (United States)

    Choi, Hyungyun; Kim, Ho

    2017-01-01

    Achieving national health equity is currently a pressing issue. Large regional variations in the health determinants are observed. Depression, one of the most common mental disorders, has large variations in incidence among different populations, and thus must be regionally analyzed. The present study aimed at analyzing regional disparities in depressive symptoms and identifying the health determinants that require regional interventions. Using health indicators of depression in the Korea Community Health Survey 2011 and 2013, the Moran's I was calculated for each variable to assess spatial autocorrelation, and a validated geographically weighted regression analysis using ArcGIS version 10.1 of different domains: health behavior, morbidity, and the social and physical environments were created, and the final model included a combination of significant variables in these models. In the health behavior domain, the weekly breakfast intake frequency of 1-2 times was the most significantly correlated with depression in all regions, followed by exposure to secondhand smoke and the level of perceived stress in some regions. In the morbidity domain, the rate of lifetime diagnosis of myocardial infarction was the most significantly correlated with depression. In the social and physical environment domain, the trust environment within the local community was highly correlated with depression, showing that lower the level of trust, higher was the level of depression. A final model was constructed and analyzed using highly influential variables from each domain. The models were divided into two groups according to the significance of correlation of each variable with the experience of depression symptoms. The indicators of the regional health status are significantly associated with the incidence of depressive symptoms within a region. The significance of this correlation varied across regions.

  3. Fast weighted centroid algorithm for single particle localization near the information limit.

    Science.gov (United States)

    Fish, Jeremie; Scrimgeour, Jan

    2015-07-10

    A simple weighting scheme that enhances the localization precision of center of mass calculations for radially symmetric intensity distributions is presented. The algorithm effectively removes the biasing that is common in such center of mass calculations. Localization precision compares favorably with other localization algorithms used in super-resolution microscopy and particle tracking, while significantly reducing the processing time and memory usage. We expect that the algorithm presented will be of significant utility when fast computationally lightweight particle localization or tracking is desired.

  4. Weighted Local Active Pixel Pattern (WLAPP for Face Recognition in Parallel Computation Environment

    Directory of Open Access Journals (Sweden)

    Gundavarapu Mallikarjuna Rao

    2013-10-01

    Full Text Available Abstract  - The availability of multi-core technology resulted totally new computational era. Researchers are keen to explore available potential in state of art-machines for breaking the bearer imposed by serial computation. Face Recognition is one of the challenging applications on so ever computational environment. The main difficulty of traditional Face Recognition algorithms is lack of the scalability. In this paper Weighted Local Active Pixel Pattern (WLAPP, a new scalable Face Recognition Algorithm suitable for parallel environment is proposed.  Local Active Pixel Pattern (LAPP is found to be simple and computational inexpensive compare to Local Binary Patterns (LBP. WLAPP is developed based on concept of LAPP. The experimentation is performed on FG-Net Aging Database with deliberately introduced 20% distortion and the results are encouraging. Keywords — Active pixels, Face Recognition, Local Binary Pattern (LBP, Local Active Pixel Pattern (LAPP, Pattern computing, parallel workers, template, weight computation.  

  5. Application of geographically-weighted regression analysis to assess risk factors for malaria hotspots in Keur Soce health and demographic surveillance site.

    Science.gov (United States)

    Ndiath, Mansour M; Cisse, Badara; Ndiaye, Jean Louis; Gomis, Jules F; Bathiery, Ousmane; Dia, Anta Tal; Gaye, Oumar; Faye, Babacar

    2015-11-18

    In Senegal, considerable efforts have been made to reduce malaria morbidity and mortality during the last decade. This resulted in a marked decrease of malaria cases. With the decline of malaria cases, transmission has become sparse in most Senegalese health districts. This study investigated malaria hotspots in Keur Soce sites by using geographically-weighted regression. Because of the occurrence of hotspots, spatial modelling of malaria cases could have a considerable effect in disease surveillance. This study explored and analysed the spatial relationships between malaria occurrence and socio-economic and environmental factors in small communities in Keur Soce, Senegal, using 6 months passive surveillance. Geographically-weighted regression was used to explore the spatial variability of relationships between malaria incidence or persistence and the selected socio-economic, and human predictors. A model comparison of between ordinary least square and geographically-weighted regression was also explored. Vector dataset (spatial) of the study area by village levels and statistical data (non-spatial) on malaria confirmed cases, socio-economic status (bed net use), population data (size of the household) and environmental factors (temperature, rain fall) were used in this exploratory analysis. ArcMap 10.2 and Stata 11 were used to perform malaria hotspots analysis. From Jun to December, a total of 408 confirmed malaria cases were notified. The explanatory variables-household size, housing materials, sleeping rooms, sheep and distance to breeding site returned significant t values of -0.25, 2.3, 4.39, 1.25 and 2.36, respectively. The OLS global model revealed that it explained about 70 % (adjusted R(2) = 0.70) of the variation in malaria occurrence with AIC = 756.23. The geographically-weighted regression of malaria hotspots resulted in coefficient intercept ranging from 1.89 to 6.22 with a median of 3.5. Large positive values are distributed mainly in the southeast

  6. Implementations of geographically weighted lasso in spatial data with multicollinearity (Case study: Poverty modeling of Java Island)

    Science.gov (United States)

    Setiyorini, Anis; Suprijadi, Jadi; Handoko, Budhi

    2017-03-01

    Geographically Weighted Regression (GWR) is a regression model that takes into account the spatial heterogeneity effect. In the application of the GWR, inference on regression coefficients is often of interest, as is estimation and prediction of the response variable. Empirical research and studies have demonstrated that local correlation between explanatory variables can lead to estimated regression coefficients in GWR that are strongly correlated, a condition named multicollinearity. It later results on a large standard error on estimated regression coefficients, and, hence, problematic for inference on relationships between variables. Geographically Weighted Lasso (GWL) is a method which capable to deal with spatial heterogeneity and local multicollinearity in spatial data sets. GWL is a further development of GWR method, which adds a LASSO (Least Absolute Shrinkage and Selection Operator) constraint in parameter estimation. In this study, GWL will be applied by using fixed exponential kernel weights matrix to establish a poverty modeling of Java Island, Indonesia. The results of applying the GWL to poverty datasets show that this method stabilizes regression coefficients in the presence of multicollinearity and produces lower prediction and estimation error of the response variable than GWR does.

  7. Least Squares Adjustment: Linear and Nonlinear Weighted Regression Analysis

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg

    2007-01-01

    This note primarily describes the mathematics of least squares regression analysis as it is often used in geodesy including land surveying and satellite positioning applications. In these fields regression is often termed adjustment. The note also contains a couple of typical land surveying...... and satellite positioning application examples. In these application areas we are typically interested in the parameters in the model typically 2- or 3-D positions and not in predictive modelling which is often the main concern in other regression analysis applications. Adjustment is often used to obtain...... the clock error) and to obtain estimates of the uncertainty with which the position is determined. Regression analysis is used in many other fields of application both in the natural, the technical and the social sciences. Examples may be curve fitting, calibration, establishing relationships between...

  8. Dual channel rank-based intensity weighting for quantitative co-localization of microscopy images

    LENUS (Irish Health Repository)

    Singan, Vasanth R

    2011-10-21

    Abstract Background Accurate quantitative co-localization is a key parameter in the context of understanding the spatial co-ordination of molecules and therefore their function in cells. Existing co-localization algorithms consider either the presence of co-occurring pixels or correlations of intensity in regions of interest. Depending on the image source, and the algorithm selected, the co-localization coefficients determined can be highly variable, and often inaccurate. Furthermore, this choice of whether co-occurrence or correlation is the best approach for quantifying co-localization remains controversial. Results We have developed a novel algorithm to quantify co-localization that improves on and addresses the major shortcomings of existing co-localization measures. This algorithm uses a non-parametric ranking of pixel intensities in each channel, and the difference in ranks of co-localizing pixel positions in the two channels is used to weight the coefficient. This weighting is applied to co-occurring pixels thereby efficiently combining both co-occurrence and correlation. Tests with synthetic data sets show that the algorithm is sensitive to both co-occurrence and correlation at varying levels of intensity. Analysis of biological data sets demonstrate that this new algorithm offers high sensitivity, and that it is capable of detecting subtle changes in co-localization, exemplified by studies on a well characterized cargo protein that moves through the secretory pathway of cells. Conclusions This algorithm provides a novel way to efficiently combine co-occurrence and correlation components in biological images, thereby generating an accurate measure of co-localization. This approach of rank weighting of intensities also eliminates the need for manual thresholding of the image, which is often a cause of error in co-localization quantification. We envisage that this tool will facilitate the quantitative analysis of a wide range of biological data sets

  9. Exploring the Non-Stationary Effects of Forests and Developed Land within Watersheds on Biological Indicators of Streams Using Geographically-Weighted Regression

    Directory of Open Access Journals (Sweden)

    Kyoung-Jin An

    2016-03-01

    Full Text Available This study examined the non-stationary relationship between the ecological condition of streams and the proportions of forest and developed land in watersheds using geographically-weighted regression (GWR. Most previous studies have adopted the ordinary least squares (OLS method, which assumes stationarity of the relationship between land use and biological indicators. However, these conventional OLS models cannot provide any insight into local variations in the land use effects within watersheds. Here, we compared the performance of the OLS and GWR statistical models applied to benthic diatom, macroinvertebrate, and fish communities in sub-watershed management areas. We extracted land use datasets from the Ministry of Environment LULC map and data on biological indicators in Nakdong river systems from the National Aquatic Ecological Monitoring Program in Korea. We found that the GWR model had superior performance compared with the OLS model, as assessed based on R2, Akaike’s Information Criterion, and Moran’s I values. Furthermore, GWR models revealed specific localized effects of land use on biological indicators, which we investigated further. The results of this study can be used to inform more effective policies on watershed management and to enhance ecological integrity by prioritizing sub-watershed management areas

  10. Analysis of the relationship between community characteristics and depression using geographically weighted regression

    Directory of Open Access Journals (Sweden)

    Hyungyun Choi

    2017-06-01

    Full Text Available OBJECTIVES Achieving national health equity is currently a pressing issue. Large regional variations in the health determinants are observed. Depression, one of the most common mental disorders, has large variations in incidence among different populations, and thus must be regionally analyzed. The present study aimed at analyzing regional disparities in depressive symptoms and identifying the health determinants that require regional interventions. METHODS Using health indicators of depression in the Korea Community Health Survey 2011 and 2013, the Moran’s I was calculated for each variable to assess spatial autocorrelation, and a validated geographically weighted regression analysis using ArcGIS version 10.1 of different domains: health behavior, morbidity, and the social and physical environments were created, and the final model included a combination of significant variables in these models. RESULTS In the health behavior domain, the weekly breakfast intake frequency of 1-2 times was the most significantly correlated with depression in all regions, followed by exposure to secondhand smoke and the level of perceived stress in some regions. In the morbidity domain, the rate of lifetime diagnosis of myocardial infarction was the most significantly correlated with depression. In the social and physical environment domain, the trust environment within the local community was highly correlated with depression, showing that lower the level of trust, higher was the level of depression. A final model was constructed and analyzed using highly influential variables from each domain. The models were divided into two groups according to the significance of correlation of each variable with the experience of depression symptoms. CONCLUSIONS The indicators of the regional health status are significantly associated with the incidence of depressive symptoms within a region. The significance of this correlation varied across regions.

  11. Health status monitoring for ICU patients based on locally weighted principal component analysis.

    Science.gov (United States)

    Ding, Yangyang; Ma, Xin; Wang, Youqing

    2018-03-01

    Intelligent status monitoring for critically ill patients can help medical stuff quickly discover and assess the changes of disease and then make appropriate treatment strategy. However, general-type monitoring model now widely used is difficult to adapt the changes of intensive care unit (ICU) patients' status due to its fixed pattern, and a more robust, efficient and fast monitoring model should be developed to the individual. A data-driven learning approach combining locally weighted projection regression (LWPR) and principal component analysis (PCA) is firstly proposed and applied to monitor the nonlinear process of patients' health status in ICU. LWPR is used to approximate the complex nonlinear process with local linear models, in which PCA could be further applied to status monitoring, and finally a global weighted statistic will be acquired for detecting the possible abnormalities. Moreover, some improved versions are developed, such as LWPR-MPCA and LWPR-JPCA, which also have superior performance. Eighteen subjects were selected from the Physiobank's Multi-parameter Intelligent Monitoring for Intensive Care II (MIMIC II) database, and two vital signs of each subject were chosen for online monitoring. The proposed method was compared with several existing methods including traditional PCA, Partial least squares (PLS), just in time learning combined with modified PCA (L-PCA), and Kernel PCA (KPCA). The experimental results demonstrated that the mean fault detection rate (FDR) of PCA can be improved by 41.7% after adding LWPR. The mean FDR of LWPR-MPCA was increased by 8.3%, compared with the latest reported method L-PCA. Meanwhile, LWPR spent less training time than others, especially KPCA. LWPR is first introduced into ICU patients monitoring and achieves the best monitoring performance including adaptability to changes in patient status, sensitivity for abnormality detection as well as its fast learning speed and low computational complexity. The algorithm

  12. Spatiotemporal Pattern of PM2.5 Concentrations in Mainland China and Analysis of Its Influencing Factors using Geographically Weighted Regression

    Science.gov (United States)

    Luo, Jieqiong; Du, Peijun; Samat, Alim; Xia, Junshi; Che, Meiqin; Xue, Zhaohui

    2017-01-01

    Based on annual average PM2.5 gridded dataset, this study first analyzed the spatiotemporal pattern of PM2.5 across Mainland China during 1998-2012. Then facilitated with meteorological site data, land cover data, population and Gross Domestic Product (GDP) data, etc., the contributions of latent geographic factors, including socioeconomic factors (e.g., road, agriculture, population, industry) and natural geographical factors (e.g., topography, climate, vegetation) to PM2.5 were explored through Geographically Weighted Regression (GWR) model. The results revealed that PM2.5 concentrations increased while the spatial pattern remained stable, and the proportion of areas with PM2.5 concentrations greater than 35 μg/m3 significantly increased from 23.08% to 29.89%. Moreover, road, agriculture, population and vegetation showed the most significant impacts on PM2.5. Additionally, the Moran’s I for the residuals of GWR was 0.025 (not significant at a 0.01 level), indicating that the GWR model was properly specified. The local coefficient estimates of GDP in some cities were negative, suggesting the existence of the inverted-U shaped Environmental Kuznets Curve (EKC) for PM2.5 in Mainland China. The effects of each latent factor on PM2.5 in various regions were different. Therefore, regional measures and strategies for controlling PM2.5 should be formulated in terms of the local impacts of specific factors.

  13. SU-F-BRD-01: A Logistic Regression Model to Predict Objective Function Weights in Prostate Cancer IMRT

    International Nuclear Information System (INIS)

    Boutilier, J; Chan, T; Lee, T; Craig, T; Sharpe, M

    2014-01-01

    Purpose: To develop a statistical model that predicts optimization objective function weights from patient geometry for intensity-modulation radiotherapy (IMRT) of prostate cancer. Methods: A previously developed inverse optimization method (IOM) is applied retrospectively to determine optimal weights for 51 treated patients. We use an overlap volume ratio (OVR) of bladder and rectum for different PTV expansions in order to quantify patient geometry in explanatory variables. Using the optimal weights as ground truth, we develop and train a logistic regression (LR) model to predict the rectum weight and thus the bladder weight. Post hoc, we fix the weights of the left femoral head, right femoral head, and an artificial structure that encourages conformity to the population average while normalizing the bladder and rectum weights accordingly. The population average of objective function weights is used for comparison. Results: The OVR at 0.7cm was found to be the most predictive of the rectum weights. The LR model performance is statistically significant when compared to the population average over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and mean voxel dose to the bladder, rectum, CTV, and PTV. On average, the LR model predicted bladder and rectum weights that are both 63% closer to the optimal weights compared to the population average. The treatment plans resulting from the LR weights have, on average, a rectum V70Gy that is 35% closer to the clinical plan and a bladder V70Gy that is 43% closer. Similar results are seen for bladder V54Gy and rectum V54Gy. Conclusion: Statistical modelling from patient anatomy can be used to determine objective function weights in IMRT for prostate cancer. Our method allows the treatment planners to begin the personalization process from an informed starting point, which may lead to more consistent clinical plans and reduce overall planning time

  14. SU-F-BRD-01: A Logistic Regression Model to Predict Objective Function Weights in Prostate Cancer IMRT

    Energy Technology Data Exchange (ETDEWEB)

    Boutilier, J; Chan, T; Lee, T [University of Toronto, Toronto, Ontario (Canada); Craig, T; Sharpe, M [University of Toronto, Toronto, Ontario (Canada); The Princess Margaret Cancer Centre - UHN, Toronto, ON (Canada)

    2014-06-15

    Purpose: To develop a statistical model that predicts optimization objective function weights from patient geometry for intensity-modulation radiotherapy (IMRT) of prostate cancer. Methods: A previously developed inverse optimization method (IOM) is applied retrospectively to determine optimal weights for 51 treated patients. We use an overlap volume ratio (OVR) of bladder and rectum for different PTV expansions in order to quantify patient geometry in explanatory variables. Using the optimal weights as ground truth, we develop and train a logistic regression (LR) model to predict the rectum weight and thus the bladder weight. Post hoc, we fix the weights of the left femoral head, right femoral head, and an artificial structure that encourages conformity to the population average while normalizing the bladder and rectum weights accordingly. The population average of objective function weights is used for comparison. Results: The OVR at 0.7cm was found to be the most predictive of the rectum weights. The LR model performance is statistically significant when compared to the population average over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and mean voxel dose to the bladder, rectum, CTV, and PTV. On average, the LR model predicted bladder and rectum weights that are both 63% closer to the optimal weights compared to the population average. The treatment plans resulting from the LR weights have, on average, a rectum V70Gy that is 35% closer to the clinical plan and a bladder V70Gy that is 43% closer. Similar results are seen for bladder V54Gy and rectum V54Gy. Conclusion: Statistical modelling from patient anatomy can be used to determine objective function weights in IMRT for prostate cancer. Our method allows the treatment planners to begin the personalization process from an informed starting point, which may lead to more consistent clinical plans and reduce overall planning time.

  15. The characterization of weighted local hardy spaces on domains and its application.

    Science.gov (United States)

    Wang, Heng-geng; Yang, Xiao-ming

    2004-09-01

    In this paper, we give the four equivalent characterizations for the weighted local hardy spaces on Lipschitz domains. Also, we give their application for the harmonic function defined in bounded Lipschitz domains.

  16. Modeling Source Water TOC Using Hydroclimate Variables and Local Polynomial Regression.

    Science.gov (United States)

    Samson, Carleigh C; Rajagopalan, Balaji; Summers, R Scott

    2016-04-19

    To control disinfection byproduct (DBP) formation in drinking water, an understanding of the source water total organic carbon (TOC) concentration variability can be critical. Previously, TOC concentrations in water treatment plant source waters have been modeled using streamflow data. However, the lack of streamflow data or unimpaired flow scenarios makes it difficult to model TOC. In addition, TOC variability under climate change further exacerbates the problem. Here we proposed a modeling approach based on local polynomial regression that uses climate, e.g. temperature, and land surface, e.g., soil moisture, variables as predictors of TOC concentration, obviating the need for streamflow. The local polynomial approach has the ability to capture non-Gaussian and nonlinear features that might be present in the relationships. The utility of the methodology is demonstrated using source water quality and climate data in three case study locations with surface source waters including river and reservoir sources. The models show good predictive skill in general at these locations, with lower skills at locations with the most anthropogenic influences in their streams. Source water TOC predictive models can provide water treatment utilities important information for making treatment decisions for DBP regulation compliance under future climate scenarios.

  17. Measuring decision weights in recognition experiments with multiple response alternatives: comparing the correlation and multinomial-logistic-regression methods.

    Science.gov (United States)

    Dai, Huanping; Micheyl, Christophe

    2012-11-01

    Psychophysical "reverse-correlation" methods allow researchers to gain insight into the perceptual representations and decision weighting strategies of individual subjects in perceptual tasks. Although these methods have gained momentum, until recently their development was limited to experiments involving only two response categories. Recently, two approaches for estimating decision weights in m-alternative experiments have been put forward. One approach extends the two-category correlation method to m > 2 alternatives; the second uses multinomial logistic regression (MLR). In this article, the relative merits of the two methods are discussed, and the issues of convergence and statistical efficiency of the methods are evaluated quantitatively using Monte Carlo simulations. The results indicate that, for a range of values of the number of trials, the estimated weighting patterns are closer to their asymptotic values for the correlation method than for the MLR method. Moreover, for the MLR method, weight estimates for different stimulus components can exhibit strong correlations, making the analysis and interpretation of measured weighting patterns less straightforward than for the correlation method. These and other advantages of the correlation method, which include computational simplicity and a close relationship to other well-established psychophysical reverse-correlation methods, make it an attractive tool to uncover decision strategies in m-alternative experiments.

  18. Two-Stage Method Based on Local Polynomial Fitting for a Linear Heteroscedastic Regression Model and Its Application in Economics

    Directory of Open Access Journals (Sweden)

    Liyun Su

    2012-01-01

    Full Text Available We introduce the extension of local polynomial fitting to the linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to nonparametric technique of local polynomial estimation, we do not need to know the heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we focus on comparison of parameters and reach an optimal fitting. Besides, we verify the asymptotic normality of parameters based on numerical simulations. Finally, this approach is applied to a case of economics, and it indicates that our method is surely effective in finite-sample situations.

  19. Weighted tunable clustering in local-world networks with increment behavior

    International Nuclear Information System (INIS)

    Ma, Ying-Hong; Li, Huijia; Zhang, Xiao-Dong

    2010-01-01

    Since some realistic networks are influenced not only by increment behavior but also by the tunable clustering mechanism with new nodes to be added to networks, it is interesting to characterize the model for those actual networks. In this paper, a weighted local-world model, which incorporates increment behavior and the tunable clustering mechanism, is proposed and its properties are investigated, such as degree distribution and clustering coefficient. Numerical simulations are fitted to the model and also display good right-skewed scale-free properties. Furthermore, the correlation of vertices in our model is studied which shows the assortative property. The epidemic spreading process by weighted transmission rate on the model shows that the tunable clustering behavior has a great impact on the epidemic dynamic

  20. A coregionalization model can assist specification of Geographically Weighted Poisson Regression: Application to an ecological study.

    Science.gov (United States)

    Ribeiro, Manuel Castro; Sousa, António Jorge; Pereira, Maria João

    2016-05-01

    The geographical distribution of health outcomes is influenced by socio-economic and environmental factors operating on different spatial scales. Geographical variations in relationships can be revealed with semi-parametric Geographically Weighted Poisson Regression (sGWPR), a model that can combine both geographically varying and geographically constant parameters. To decide whether a parameter should vary geographically, two models are compared: one in which all parameters are allowed to vary geographically and one in which all except the parameter being evaluated are allowed to vary geographically. The model with the lower corrected Akaike Information Criterion (AICc) is selected. Delivering model selection exclusively according to the AICc might hide important details in spatial variations of associations. We propose assisting the decision by using a Linear Model of Coregionalization (LMC). Here we show how LMC can refine sGWPR on ecological associations between socio-economic and environmental variables and low birth weight outcomes in the west-north-central region of Portugal. Copyright © 2016 Elsevier Ltd. All rights reserved.

  1. Integrating classification trees with local logistic regression in Intensive Care prognosis.

    Science.gov (United States)

    Abu-Hanna, Ameen; de Keizer, Nicolette

    2003-01-01

    Health care effectiveness and efficiency are under constant scrutiny especially when treatment is quite costly as in the Intensive Care (IC). Currently there are various international quality of care programs for the evaluation of IC. At the heart of such quality of care programs lie prognostic models whose prediction of patient mortality can be used as a norm to which actual mortality is compared. The current generation of prognostic models in IC are statistical parametric models based on logistic regression. Given a description of a patient at admission, these models predict the probability of his or her survival. Typically, this patient description relies on an aggregate variable, called a score, that quantifies the severity of illness of the patient. The use of a parametric model and an aggregate score form adequate means to develop models when data is relatively scarce but it introduces the risk of bias. This paper motivates and suggests a method for studying and improving the performance behavior of current state-of-the-art IC prognostic models. Our method is based on machine learning and statistical ideas and relies on exploiting information that underlies a score variable. In particular, this underlying information is used to construct a classification tree whose nodes denote patient sub-populations. For these sub-populations, local models, most notably logistic regression ones, are developed using only the total score variable. We compare the performance of this hybrid model to that of a traditional global logistic regression model. We show that the hybrid model not only provides more insight into the data but also has a better performance. We pay special attention to the precision aspect of model performance and argue why precision is more important than discrimination ability.

  2. Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization

    Directory of Open Access Journals (Sweden)

    José R. Casar

    2011-09-01

    Full Text Available The practical deployment of wireless positioning systems requires minimizing the calibration procedures while improving the location estimation accuracy. Received Signal Strength localization techniques using propagation channel models are the simplest alternative, but they are usually designed under the assumption that the radio propagation model is to be perfectly characterized a priori. In practice, this assumption does not hold and the localization results are affected by the inaccuracies of the theoretical, roughly calibrated or just imperfect channel models used to compute location. In this paper, we propose the use of weighted multilateration techniques to gain robustness with respect to these inaccuracies, reducing the dependency of having an optimal channel model. In particular, we propose two weighted least squares techniques based on the standard hyperbolic and circular positioning algorithms that specifically consider the accuracies of the different measurements to obtain a better estimation of the position. These techniques are compared to the standard hyperbolic and circular positioning techniques through both numerical simulations and an exhaustive set of real experiments on different types of wireless networks (a wireless sensor network, a WiFi network and a Bluetooth network. The algorithms not only produce better localization results with a very limited overhead in terms of computational cost but also achieve a greater robustness to inaccuracies in channel modeling.

  3. Targeting: Logistic Regression, Special Cases and Extensions

    Directory of Open Access Journals (Sweden)

    Helmut Schaeben

    2014-12-01

    Full Text Available Logistic regression is a classical linear model for logit-transformed conditional probabilities of a binary target variable. It recovers the true conditional probabilities if the joint distribution of predictors and the target is of log-linear form. Weights-of-evidence is an ordinary logistic regression with parameters equal to the differences of the weights of evidence if all predictor variables are discrete and conditionally independent given the target variable. The hypothesis of conditional independence can be tested in terms of log-linear models. If the assumption of conditional independence is violated, the application of weights-of-evidence does not only corrupt the predicted conditional probabilities, but also their rank transform. Logistic regression models, including the interaction terms, can account for the lack of conditional independence, appropriate interaction terms compensate exactly for violations of conditional independence. Multilayer artificial neural nets may be seen as nested regression-like models, with some sigmoidal activation function. Most often, the logistic function is used as the activation function. If the net topology, i.e., its control, is sufficiently versatile to mimic interaction terms, artificial neural nets are able to account for violations of conditional independence and yield very similar results. Weights-of-evidence cannot reasonably include interaction terms; subsequent modifications of the weights, as often suggested, cannot emulate the effect of interaction terms.

  4. Selecting the correct weighting factors for linear and quadratic calibration curves with least-squares regression algorithm in bioanalytical LC-MS/MS assays and impacts of using incorrect weighting factors on curve stability, data quality, and assay performance.

    Science.gov (United States)

    Gu, Huidong; Liu, Guowen; Wang, Jian; Aubry, Anne-Françoise; Arnold, Mark E

    2014-09-16

    A simple procedure for selecting the correct weighting factors for linear and quadratic calibration curves with least-squares regression algorithm in bioanalytical LC-MS/MS assays is reported. The correct weighting factor is determined by the relationship between the standard deviation of instrument responses (σ) and the concentrations (x). The weighting factor of 1, 1/x, or 1/x(2) should be selected if, over the entire concentration range, σ is a constant, σ(2) is proportional to x, or σ is proportional to x, respectively. For the first time, we demonstrated with detailed scientific reasoning, solid historical data, and convincing justification that 1/x(2) should always be used as the weighting factor for all bioanalytical LC-MS/MS assays. The impacts of using incorrect weighting factors on curve stability, data quality, and assay performance were thoroughly investigated. It was found that the most stable curve could be obtained when the correct weighting factor was used, whereas other curves using incorrect weighting factors were unstable. It was also found that there was a very insignificant impact on the concentrations reported with calibration curves using incorrect weighting factors as the concentrations were always reported with the passing curves which actually overlapped with or were very close to the curves using the correct weighting factor. However, the use of incorrect weighting factors did impact the assay performance significantly. Finally, the difference between the weighting factors of 1/x(2) and 1/y(2) was discussed. All of the findings can be generalized and applied into other quantitative analysis techniques using calibration curves with weighted least-squares regression algorithm.

  5. A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey

    Science.gov (United States)

    Ozdemir, Adnan; Altural, Tolga

    2013-03-01

    This study evaluated and compared landslide susceptibility maps produced with three different methods, frequency ratio, weights of evidence, and logistic regression, by using validation datasets. The field surveys performed as part of this investigation mapped the locations of 90 landslides that had been identified in the Sultan Mountains of south-western Turkey. The landslide influence parameters used for this study are geology, relative permeability, land use/land cover, precipitation, elevation, slope, aspect, total curvature, plan curvature, profile curvature, wetness index, stream power index, sediment transportation capacity index, distance to drainage, distance to fault, drainage density, fault density, and spring density maps. The relationships between landslide distributions and these parameters were analysed using the three methods, and the results of these methods were then used to calculate the landslide susceptibility of the entire study area. The accuracy of the final landslide susceptibility maps was evaluated based on the landslides observed during the fieldwork, and the accuracy of the models was evaluated by calculating each model's relative operating characteristic curve. The predictive capability of each model was determined from the area under the relative operating characteristic curve and the areas under the curves obtained using the frequency ratio, logistic regression, and weights of evidence methods are 0.976, 0.952, and 0.937, respectively. These results indicate that the frequency ratio and weights of evidence models are relatively good estimators of landslide susceptibility in the study area. Specifically, the results of the correlation analysis show a high correlation between the frequency ratio and weights of evidence results, and the frequency ratio and logistic regression methods exhibit correlation coefficients of 0.771 and 0.727, respectively. The frequency ratio model is simple, and its input, calculation and output processes are

  6. Centroid Localization of Uncooperative Nodes in Wireless Networks Using a Relative Span Weighting Method

    Directory of Open Access Journals (Sweden)

    Christine Laurendeau

    2010-01-01

    Full Text Available Increasingly ubiquitous wireless technologies require novel localization techniques to pinpoint the position of an uncooperative node, whether the target is a malicious device engaging in a security exploit or a low-battery handset in the middle of a critical emergency. Such scenarios necessitate that a radio signal source be localized by other network nodes efficiently, using minimal information. We propose two new algorithms for estimating the position of an uncooperative transmitter, based on the received signal strength (RSS of a single target message at a set of receivers whose coordinates are known. As an extension to the concept of centroid localization, our mechanisms weigh each receiver's coordinates based on the message's relative RSS at that receiver, with respect to the span of RSS values over all receivers. The weights may decrease from the highest RSS receiver either linearly or exponentially. Our simulation results demonstrate that for all but the most sparsely populated wireless networks, our exponentially weighted mechanism localizes a target node within the regulations stipulated for emergency services location accuracy.

  7. Short-term weight loss attenuates local tissue inflammation and improves insulin sensitivity without affecting adipose inflammation in obese mice.

    Science.gov (United States)

    Jung, Dae Young; Ko, Hwi Jin; Lichtman, Eben I; Lee, Eunjung; Lawton, Elizabeth; Ong, Helena; Yu, Kristine; Azuma, Yoshihiro; Friedline, Randall H; Lee, Ki Won; Kim, Jason K

    2013-05-01

    Obesity is a major cause of insulin resistance, and weight loss is shown to improve glucose homeostasis. But the underlying mechanism and the role of inflammation remain unclear. Male C57BL/6 mice were fed a high-fat diet (HFD) for 12 wk. After HFD, weight loss was induced by changing to a low-fat diet (LFD) or exercise with continuous HFD. The weight loss effects on energy balance and insulin sensitivity were determined using metabolic cages and hyperinsulinemic euglycemic clamps in awake mice. Diet and exercise intervention for 3 wk caused a modest weight loss and improved glucose homeostasis. Weight loss dramatically reduced local inflammation in skeletal muscle, liver, and heart but not in adipose tissue. Exercise-mediated weight loss increased muscle glucose metabolism without affecting Akt phosphorylation or lipid levels. LFD-mediated weight loss reduced lipid levels and improved insulin sensitivity selectively in liver. Both weight loss interventions improved cardiac glucose metabolism. These results demonstrate that a short-term weight loss with exercise or diet intervention attenuates obesity-induced local inflammation and selectively improves insulin sensitivity in skeletal muscle and liver. Our findings suggest that local factors, not adipose tissue inflammation, are involved in the beneficial effects of weight loss on glucose homeostasis.

  8. Assessment of Brown Bear\\'s (Ursus arctos syriacus Winter Habitat Using Geographically Weighted Regression and Generalized Linear Model in South of Iran

    Directory of Open Access Journals (Sweden)

    A. A. Zarei

    2016-03-01

    Full Text Available Winter dens are one of the important components of brown bear's (Ursus arctos syriacus habitat, affecting their reproduction and survival. Therefore identification of factors affecting the habitat selection and suitable denning areas in the conservation of our largest carnivore is necessary. We used Geographically Weighted Logistic Regression (GWLR and Generalized Linear Model (GLM for modeling suitability of denning habitat in Kouhkhom region in Fars province. In the present research, 20 dens (presence locations and 20 caves where signs of bear were not found (absence locations were used as dependent variables and six environmental factors were used for each location as independent variables. The results of GLM showed that variables of distance to settlements, altitude, and distance to water were the most important parameters affecting suitability of the brown bear's denning habitat. The results of GWLR showed the significant local variations in the relationship between occurrence of brown bear dens and the variable of distance to settlements. Based on the results of both models, suitable habitats for denning of the species are impassable areas in the mountains and inaccessible for humans.

  9. Education-Based Gaps in eHealth: A Weighted Logistic Regression Approach.

    Science.gov (United States)

    Amo, Laura

    2016-10-12

    Persons with a college degree are more likely to engage in eHealth behaviors than persons without a college degree, compounding the health disadvantages of undereducated groups in the United States. However, the extent to which quality of recent eHealth experience reduces the education-based eHealth gap is unexplored. The goal of this study was to examine how eHealth information search experience moderates the relationship between college education and eHealth behaviors. Based on a nationally representative sample of adults who reported using the Internet to conduct the most recent health information search (n=1458), I evaluated eHealth search experience in relation to the likelihood of engaging in different eHealth behaviors. I examined whether Internet health information search experience reduces the eHealth behavior gaps among college-educated and noncollege-educated adults. Weighted logistic regression models were used to estimate the probability of different eHealth behaviors. College education was significantly positively related to the likelihood of 4 eHealth behaviors. In general, eHealth search experience was negatively associated with health care behaviors, health information-seeking behaviors, and user-generated or content sharing behaviors after accounting for other covariates. Whereas Internet health information search experience has narrowed the education gap in terms of likelihood of using email or Internet to communicate with a doctor or health care provider and likelihood of using a website to manage diet, weight, or health, it has widened the education gap in the instances of searching for health information for oneself, searching for health information for someone else, and downloading health information on a mobile device. The relationship between college education and eHealth behaviors is moderated by Internet health information search experience in different ways depending on the type of eHealth behavior. After controlling for college

  10. Uncertainty of pesticide residue concentration determined from ordinary and weighted linear regression curve.

    Science.gov (United States)

    Yolci Omeroglu, Perihan; Ambrus, Árpad; Boyacioglu, Dilek

    2018-03-28

    Determination of pesticide residues is based on calibration curves constructed for each batch of analysis. Calibration standard solutions are prepared from a known amount of reference material at different concentration levels covering the concentration range of the analyte in the analysed samples. In the scope of this study, the applicability of both ordinary linear and weighted linear regression (OLR and WLR) for pesticide residue analysis was investigated. We used 782 multipoint calibration curves obtained for 72 different analytical batches with high-pressure liquid chromatography equipped with an ultraviolet detector, and gas chromatography with electron capture, nitrogen phosphorus or mass spectrophotometer detectors. Quality criteria of the linear curves including regression coefficient, standard deviation of relative residuals and deviation of back calculated concentrations were calculated both for WLR and OLR methods. Moreover, the relative uncertainty of the predicted analyte concentration was estimated for both methods. It was concluded that calibration curve based on WLR complies with all the quality criteria set by international guidelines compared to those calculated with OLR. It means that all the data fit well with WLR for pesticide residue analysis. It was estimated that, regardless of the actual concentration range of the calibration, relative uncertainty at the lowest calibrated level ranged between 0.3% and 113.7% for OLR and between 0.2% and 22.1% for WLR. At or above 1/3 of the calibrated range, uncertainty of calibration curve ranged between 0.1% and 16.3% for OLR and 0% and 12.2% for WLR, and therefore, the two methods gave comparable results.

  11. Robust mislabel logistic regression without modeling mislabel probabilities.

    Science.gov (United States)

    Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

    2018-03-01

    Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

  12. Unified heat kernel regression for diffusion, kernel smoothing and wavelets on manifolds and its application to mandible growth modeling in CT images.

    Science.gov (United States)

    Chung, Moo K; Qiu, Anqi; Seo, Seongho; Vorperian, Houri K

    2015-05-01

    We present a novel kernel regression framework for smoothing scalar surface data using the Laplace-Beltrami eigenfunctions. Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights. The new kernel method is mathematically equivalent to isotropic heat diffusion, kernel smoothing and recently popular diffusion wavelets. The numerical implementation is validated on a unit sphere using spherical harmonics. As an illustration, the method is applied to characterize the localized growth pattern of mandible surfaces obtained in CT images between ages 0 and 20 by regressing the length of displacement vectors with respect to a surface template. Copyright © 2015 Elsevier B.V. All rights reserved.

  13. Estimation of genotype X environment interactions, in a grassbased system, for milk yield, body condition score,and body weight using random regression models

    NARCIS (Netherlands)

    Berry, D.P.; Buckley, F.; Dillon, P.; Evans, R.D.; Rath, M.; Veerkamp, R.F.

    2003-01-01

    (Co)variance components for milk yield, body condition score (BCS), body weight (BW), BCS change and BW change over different herd-year mean milk yields (HMY) and nutritional environments (concentrate feeding level, grazing severity and silage quality) were estimated using a random regression model.

  14. An improved geographically weighted regression model for PM2.5 concentration estimation in large areas

    Science.gov (United States)

    Zhai, Liang; Li, Shuang; Zou, Bin; Sang, Huiyong; Fang, Xin; Xu, Shan

    2018-05-01

    Considering the spatial non-stationary contributions of environment variables to PM2.5 variations, the geographically weighted regression (GWR) modeling method has been using to estimate PM2.5 concentrations widely. However, most of the GWR models in reported studies so far were established based on the screened predictors through pretreatment correlation analysis, and this process might cause the omissions of factors really driving PM2.5 variations. This study therefore developed a best subsets regression (BSR) enhanced principal component analysis-GWR (PCA-GWR) modeling approach to estimate PM2.5 concentration by fully considering all the potential variables' contributions simultaneously. The performance comparison experiment between PCA-GWR and regular GWR was conducted in the Beijing-Tianjin-Hebei (BTH) region over a one-year-period. Results indicated that the PCA-GWR modeling outperforms the regular GWR modeling with obvious higher model fitting- and cross-validation based adjusted R2 and lower RMSE. Meanwhile, the distribution map of PM2.5 concentration from PCA-GWR modeling also clearly depicts more spatial variation details in contrast to the one from regular GWR modeling. It can be concluded that the BSR enhanced PCA-GWR modeling could be a reliable way for effective air pollution concentration estimation in the coming future by involving all the potential predictor variables' contributions to PM2.5 variations.

  15. Age and body weight of Moroccan local cattle at puberty: Effect of season

    International Nuclear Information System (INIS)

    Mazouz, A.; Asri, A.

    1988-01-01

    Moroccan local cattle are a distinct breed comprising almost 90% of the total cattle population of 2.5 million. The age and the body weight of some 1662 heifers attaining puberty were determined under two production systems by monitoring plasma progesterone levels as an indicator of the first ovulation. The effect of season on these parameters was also studied. Ovarian cyclicity commenced in 50% of heifers by the age of 16.5 months and at a body weight of 144 kg (70% of the mature body weight). The time of puberty was correlated with both age and body weight and was influenced by both the season of the year at which puberty was reached and the system of rearing. The mean age at which behavioural oestrus was first observed and the heifer inseminated was 26.5 months. Fifty per cent of 546 heifers were pregnant by 27 months of age. (author). 18 refs, 7 figs

  16. Trajectories of childhood weight gain: the relative importance of local environment versus individual social and early life factors.

    Directory of Open Access Journals (Sweden)

    Megan A Carter

    Full Text Available To determine the association between local environmental factors with child weight status in a longitudinal study, using a semi-parametric, group-based method, while also considering social and early life factors.Standardized, directly measured BMI from 4-10 y of age, and group-based trajectory modeling (PROC TRAJ were used to estimate developmental trajectories of weight change in a Québec birth cohort (n = 1,566. Associations between the weight trajectories and living location, social cohesion, disorder, and material and social deprivation were estimated after controlling for social and early life factors.FOUR WEIGHT TRAJECTORY GROUPS WERE ESTIMATED: low-increasing (9.7%; low-medium, accelerating (36.2%; medium-high, increasing (43.0%; and high-stable (11.1%. In the low-increasing and medium-high trajectory groups, living in a semi-urban area was inversely related to weight, while living in a rural area was positively related to weight in the high-stable group. Disorder was inversely related to weight in the low-increasing group only. Other important risk factors for high-stable weight included obesity status of the mother, smoking during pregnancy, and overeating behaviors.In this study, associations between local environment factors and weight differed by trajectory group. Early life factors appear to play a more consistent role in weight status. Further work is needed to determine the influence of place on child weight.

  17. Multivariate Regression Analysis and Slaughter Livestock,

    Science.gov (United States)

    AGRICULTURE, *ECONOMICS), (*MEAT, PRODUCTION), MULTIVARIATE ANALYSIS, REGRESSION ANALYSIS , ANIMALS, WEIGHT, COSTS, PREDICTIONS, STABILITY, MATHEMATICAL MODELS, STORAGE, BEEF, PORK, FOOD, STATISTICAL DATA, ACCURACY

  18. Task demands affect spatial reference frame weighting during tactile localization in sighted and congenitally blind adults

    OpenAIRE

    Heed, Tobias; Roeder, Brigitte; Badde, Stephanie; Schubert, Jonathan

    2017-01-01

    Task demands modulate tactile localization in sighted humans, presumably through weight adjustments in the spatial integration of anatomical, skin-based, and external, posture-based information. In contrast, previous studies have suggested that congenitally blind humans, by default, refrain from automatic spatial integration and localize touch using only skin-based information. Here, sighted and congenitally blind participants localized tactile targets on the palm or back of one hand, while i...

  19. Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models

    OpenAIRE

    Fang, Chuanglin; Liu, Haimeng; Li, Guangdong; Sun, Dongqi; Miao, Zhuang

    2015-01-01

    Urban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI) values at the city level, and employed Ordinary Least Squares (OLS), Spatial Lag Model (SAR), and Geographically Weighted Regression (GWR) to quantitatively estimate the comprehensive impact and spatial variati...

  20. Tools to support interpreting multiple regression in the face of multicollinearity.

    Science.gov (United States)

    Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K

    2012-01-01

    While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.

  1. Adaptive weighted local textural features for illumination, expression, and occlusion invariant face recognition

    Science.gov (United States)

    Cui, Chen; Asari, Vijayan K.

    2014-03-01

    Biometric features such as fingerprints, iris patterns, and face features help to identify people and restrict access to secure areas by performing advanced pattern analysis and matching. Face recognition is one of the most promising biometric methodologies for human identification in a non-cooperative security environment. However, the recognition results obtained by face recognition systems are a affected by several variations that may happen to the patterns in an unrestricted environment. As a result, several algorithms have been developed for extracting different facial features for face recognition. Due to the various possible challenges of data captured at different lighting conditions, viewing angles, facial expressions, and partial occlusions in natural environmental conditions, automatic facial recognition still remains as a difficult issue that needs to be resolved. In this paper, we propose a novel approach to tackling some of these issues by analyzing the local textural descriptions for facial feature representation. The textural information is extracted by an enhanced local binary pattern (ELBP) description of all the local regions of the face. The relationship of each pixel with respect to its neighborhood is extracted and employed to calculate the new representation. ELBP reconstructs a much better textural feature extraction vector from an original gray level image in different lighting conditions. The dimensionality of the texture image is reduced by principal component analysis performed on each local face region. Each low dimensional vector representing a local region is now weighted based on the significance of the sub-region. The weight of each sub-region is determined by employing the local variance estimate of the respective region, which represents the significance of the region. The final facial textural feature vector is obtained by concatenating the reduced dimensional weight sets of all the modules (sub-regions) of the face image

  2. Detection of gene-environment interactions in the presence of linkage disequilibrium and noise by using genetic risk scores with internal weights from elastic net regression.

    Science.gov (United States)

    Hüls, Anke; Ickstadt, Katja; Schikowski, Tamara; Krämer, Ursula

    2017-06-12

    For the analysis of gene-environment (GxE) interactions commonly single nucleotide polymorphisms (SNPs) are used to characterize genetic susceptibility, an approach that mostly lacks power and has poor reproducibility. One promising approach to overcome this problem might be the use of weighted genetic risk scores (GRS), which are defined as weighted sums of risk alleles of gene variants. The gold-standard is to use external weights from published meta-analyses. In this study, we used internal weights from the marginal genetic effects of the SNPs estimated by a multivariate elastic net regression and thereby provided a method that can be used if there are no external weights available. We conducted a simulation study for the detection of GxE interactions and compared power and type I error of single SNPs analyses with Bonferroni correction and corresponding analysis with unweighted and our weighted GRS approach in scenarios with six risk SNPs and an increasing number of highly correlated (up to 210) and noise SNPs (up to 840). Applying weighted GRS increased the power enormously in comparison to the common single SNPs approach (e.g. 94.2% vs. 35.4%, respectively, to detect a weak interaction with an OR ≈ 1.04 for six uncorrelated risk SNPs and n = 700 with a well-controlled type I error). Furthermore, weighted GRS outperformed the unweighted GRS, in particular in the presence of SNPs without any effect on the phenotype (e.g. 90.1% vs. 43.9%, respectively, when 20 noise SNPs were added to the six risk SNPs). This outperforming of the weighted GRS was confirmed in a real data application on lung inflammation in the SALIA cohort (n = 402). However, in scenarios with a high number of noise SNPs (>200 vs. 6 risk SNPs), larger sample sizes are needed to avoid an increased type I error, whereas a high number of correlated SNPs can be handled even in small samples (e.g. n = 400). In conclusion, weighted GRS with weights from the marginal genetic effects of the

  3. A geographically weighted regression model for geothermal potential assessment in mediterranean cultural landscape

    Science.gov (United States)

    D'Arpa, S.; Zaccarelli, N.; Bruno, D. E.; Leucci, G.; Uricchio, V. F.; Zurlini, G.

    2012-04-01

    Geothermal heat can be used directly in many applications (agro-industrial processes, sanitary hot water production, heating/cooling systems, etc.). These applications respond to energetic and environmental sustainability criteria, ensuring substantial energy savings with low environmental impacts. In particular, in Mediterranean cultural landscapes the exploitation of geothermal energy offers a valuable alternative compared to other exploitation systems more land-consuming and visual-impact. However, low enthalpy geothermal energy applications at regional scale, require careful design and planning to fully exploit benefits and reduce drawbacks. We propose a first example of application of a Geographically Weighted Regression (GWR) for the modeling of geothermal potential in the Apulia Region (South Italy) by integrating hydrological (e.g. depth to water table, water speed and temperature), geological-geotechnical (e.g. lithology, thermal conductivity) parameters and land-use indicators. The GWR model can effectively cope with data quality, spatial anisotropy, lack of stationarity and presence of discontinuities in the underlying data maps. The geothermal potential assessment required a good knowledge of the space-time variation of the numerous parameters related to the status of geothermal resource, a contextual analysis of spatial and environmental features, as well as the presence and nature of regulations or infrastructures constraints. We create an ad hoc geodatabase within ArcGIS 10 collecting relevant data and performing a quality assessment. Cross-validation shows high level of consistency of the spatial local models, as well as error maps can depict areas of lower reliability. Based on low enthalpy geothermal potential map created, a first zoning of the study area is proposed, considering four level of possible exploitation. Such zoning is linked and refined by the actual legal constraints acting at regional or province level as enforced by the regional

  4. Simultaneous Estimation of Regression Functions for Marine Corps Technical Training Specialties.

    Science.gov (United States)

    Dunbar, Stephen B.; And Others

    This paper considers the application of Bayesian techniques for simultaneous estimation to the specification of regression weights for selection tests used in various technical training courses in the Marine Corps. Results of a method for m-group regression developed by Molenaar and Lewis (1979) suggest that common weights for training courses…

  5. Association between body weight and dimensional shell traits of ...

    African Journals Online (AJOL)

    The results of regression models revealed that live weight of A. achatina was best predicted with multiple linear regression models, while with live weight of A. marginata was best predicted with simple linear regression model and multiple linear regression models. A test of accuracy of the linear regression models showed ...

  6. Disability weights from a household survey in a low socio-economic setting: how does it compare to the global burden of disease 2010 study?

    Science.gov (United States)

    Neethling, Ian; Jelsma, Jennifer; Ramma, Lebogang; Schneider, Helen; Bradshaw, Debbie

    2016-01-01

    The global burden of disease (GBD) 2010 study used a universal set of disability weights to estimate disability adjusted life years (DALYs) by country. However, it is not clear whether these weights can be applied universally in calculating DALYs to inform local decision-making. This study derived disability weights for a resource-constrained community in Cape Town, South Africa, and interrogated whether the GBD 2010 disability weights necessarily represent the preferences of economically disadvantaged communities. A household survey was conducted in Lavender Hill, Cape Town, to assess the health state preferences of the general public. The responses from a paired comparison valuation method were assessed using a probit regression. The probit coefficients were anchored onto the 0 to 1 disability weight scale by running a lowess regression on the GBD 2010 disability weights and interpolating the coefficients between the upper and lower limit of the smoothed disability weights. Heroin and opioid dependence had the highest disability weight of 0.630, whereas intellectual disability had the lowest (0.040). Untreated injuries ranked higher than severe mental disorders. There were some counterintuitive results, such as moderate (15th) and severe vision impairment (16th) ranking higher than blindness (20th). A moderate correlation between the disability weights of the local study and those of the GBD 2010 study was observed (R(2)=0.440, pdisability weights (0.488 in local study and 0.043 in GBD 2010). Respondents seemed to value physical mobility higher than cognitive functioning, which is in contrast to the GBD 2010 study. This study shows that not all health state preferences are universal. Studies estimating DALYs need to derive local disability weights using methods that are less cognitively demanding for respondents.

  7. Long-term weight gain and economic impact in pigs castrated under local anaesthesia

    Directory of Open Access Journals (Sweden)

    F.G. Telles

    2016-12-01

    Full Text Available Castration is a controversial practice in swine production because in some countries is still performed without anaesthesia, and therefore causes intense suffering and stress to animals. This study investigated the effect of pre-surgical administration of local anaesthesia (LA on the growth performance of piglets until the end of the growth phase (102 days. Piglets aged 3 to 5 days were selected in pairs of similar weights and same age. They were originated from 22 litters. The groups were randomly assigned to one of two treatments. Castration was performed with (LA; n = 45 or without (NLA; n = 45 intra-testicular administration of 0.5 mL of 2% lidocaine plus adrenaline per testicle, administered by an automatic repeating vaccinator. Castration was performed 10 min later. Average daily weight gain and economic impact were evaluated between the intervals before castration until 21 (weaning phase, before castration until 60 (end of the initial nursery phase and before castration until 102 (growth phase days of age. Average daily weight gain data were analyzed by comparing the average daily weight gain between the weaning phase, 60 and 102 days of age versus the initial weight (pre-castration. At the end of the growing phase, animals treated with LA showed greater weight gain than animals castrated without anaesthesia. LA also showed improved cost:benefit ratio and theore might provide greater economic benefit under the conditions used in this study. Our findings have proved that castration with LA improves long-term weight gain of piglets.

  8. Birth weight recovery among very low birth weight infants surviving ...

    African Journals Online (AJOL)

    A multiple linear regression showed a negative association between ZSW at discharge and number of days nil per os without parenteral nutrition (PN). Antenatal steroids were associated with poor GV. There were no factors associated with regaining birth weight after 21 days on multiple logistic regression. Conclusion.

  9. Expression-robust 3D face recognition via weighted sparse representation of multi-scale and multi-component local normal patterns

    KAUST Repository

    Li, Huibin

    2014-06-01

    In the theory of differential geometry, surface normal, as a first order surface differential quantity, determines the orientation of a surface at each point and contains informative local surface shape information. To fully exploit this kind of information for 3D face recognition (FR), this paper proposes a novel highly discriminative facial shape descriptor, namely multi-scale and multi-component local normal patterns (MSMC-LNP). Given a normalized facial range image, three components of normal vectors are first estimated, leading to three normal component images. Then, each normal component image is encoded locally to local normal patterns (LNP) on different scales. To utilize spatial information of facial shape, each normal component image is divided into several patches, and their LNP histograms are computed and concatenated according to the facial configuration. Finally, each original facial surface is represented by a set of LNP histograms including both global and local cues. Moreover, to make the proposed solution robust to the variations of facial expressions, we propose to learn the weight of each local patch on a given encoding scale and normal component image. Based on the learned weights and the weighted LNP histograms, we formulate a weighted sparse representation-based classifier (W-SRC). In contrast to the overwhelming majority of 3D FR approaches which were only benchmarked on the FRGC v2.0 database, we carried out extensive experiments on the FRGC v2.0, Bosphorus, BU-3DFE and 3D-TEC databases, thus including 3D face data captured in different scenarios through various sensors and depicting in particular different challenges with respect to facial expressions. The experimental results show that the proposed approach consistently achieves competitive rank-one recognition rates on these databases despite their heterogeneous nature, and thereby demonstrates its effectiveness and its generalizability. © 2014 Elsevier B.V.

  10. Estimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection.

    Science.gov (United States)

    Kim, Sanghong; Kano, Manabu; Nakagawa, Hiroshi; Hasebe, Shinji

    2011-12-15

    Development of quality estimation models using near infrared spectroscopy (NIRS) and multivariate analysis has been accelerated as a process analytical technology (PAT) tool in the pharmaceutical industry. Although linear regression methods such as partial least squares (PLS) are widely used, they cannot always achieve high estimation accuracy because physical and chemical properties of a measuring object have a complex effect on NIR spectra. In this research, locally weighted PLS (LW-PLS) which utilizes a newly defined similarity between samples is proposed to estimate active pharmaceutical ingredient (API) content in granules for tableting. In addition, a statistical wavelength selection method which quantifies the effect of API content and other factors on NIR spectra is proposed. LW-PLS and the proposed wavelength selection method were applied to real process data provided by Daiichi Sankyo Co., Ltd., and the estimation accuracy was improved by 38.6% in root mean square error of prediction (RMSEP) compared to the conventional PLS using wavelengths selected on the basis of variable importance on the projection (VIP). The results clearly show that the proposed calibration modeling technique is useful for API content estimation and is superior to the conventional one. Copyright © 2011 Elsevier B.V. All rights reserved.

  11. Multi-trait and random regression mature weight heritability and ...

    African Journals Online (AJOL)

    Legendre polynomials of orders 4, 3, 6 and 3 were used for animal and maternal genetic and permanent environmental effects, respectively, considering five classes of residual variances. Mature weight (five years) direct heritability estimates were 0.35 (MM) and 0.38 (RRM). Rank correlation between sires' breeding values ...

  12. Interpreting Bivariate Regression Coefficients: Going beyond the Average

    Science.gov (United States)

    Halcoussis, Dennis; Phillips, G. Michael

    2010-01-01

    Statistics, econometrics, investment analysis, and data analysis classes often review the calculation of several types of averages, including the arithmetic mean, geometric mean, harmonic mean, and various weighted averages. This note shows how each of these can be computed using a basic regression framework. By recognizing when a regression model…

  13. Use of diffusion-weighted MRI to modify radiosurgery planning in brain metastases may reduce local recurrence.

    Science.gov (United States)

    Zakaria, Rasheed; Pomschar, Andreas; Jenkinson, Michael D; Tonn, Jörg-Christian; Belka, Claus; Ertl-Wagner, Birgit; Niyazi, Maximilian

    2017-02-01

    Stereotactic radiosurgery (SRS) is an effective and well tolerated treatment for selected brain metastases; however, local recurrence still occurs. We investigated the use of diffusion weighted MRI (DWI) as an adjunct for SRS treatment planning in brain metastases. Seventeen consecutive patients undergoing complete surgical resection of a solitary brain metastasis underwent image analysis retrospectively. SRS treatment plans were generated based on standard 3D post-contrast T1-weighted sequences at 1.5T and then separately using apparent diffusion coefficient (ADC) maps in a blinded fashion. Control scans immediately post operation confirmed complete tumour resection. Treatment plans were compared to one another and with volume of local recurrence at progression quantitatively and qualitatively by calculating the conformity index (CI), the overlapping volume as a proportion of the total combined volume, where 1 = identical plans and 0 = no conformation whatsoever. Gross tumour volumes (GTVs) using ADC and post-contrast T1-weighted sequences were quantitatively the same (related samples Wilcoxon signed rank test = -0.45, p = 0.653) but showed differing conformations (CI 0.53, p recurrence than the standard plan (median 3.53 cm 3 vs. 3.84 cm 3 , p = 0.002). ADC maps may be a useful tool in addition to the standard post-contrast T1-weighted sequence used for SRS planning.

  14. Two-step superresolution approach for surveillance face image through radial basis function-partial least squares regression and locality-induced sparse representation

    Science.gov (United States)

    Jiang, Junjun; Hu, Ruimin; Han, Zhen; Wang, Zhongyuan; Chen, Jun

    2013-10-01

    Face superresolution (SR), or face hallucination, refers to the technique of generating a high-resolution (HR) face image from a low-resolution (LR) one with the help of a set of training examples. It aims at transcending the limitations of electronic imaging systems. Applications of face SR include video surveillance, in which the individual of interest is often far from cameras. A two-step method is proposed to infer a high-quality and HR face image from a low-quality and LR observation. First, we establish the nonlinear relationship between LR face images and HR ones, according to radial basis function and partial least squares (RBF-PLS) regression, to transform the LR face into the global face space. Then, a locality-induced sparse representation (LiSR) approach is presented to enhance the local facial details once all the global faces for each LR training face are constructed. A comparison of some state-of-the-art SR methods shows the superiority of the proposed two-step approach, RBF-PLS global face regression followed by LiSR-based local patch reconstruction. Experiments also demonstrate the effectiveness under both simulation conditions and some real conditions.

  15. Time-varying effect moderation using the structural nested mean model: estimation using inverse-weighted regression with residuals

    Science.gov (United States)

    Almirall, Daniel; Griffin, Beth Ann; McCaffrey, Daniel F.; Ramchand, Rajeev; Yuen, Robert A.; Murphy, Susan A.

    2014-01-01

    This article considers the problem of examining time-varying causal effect moderation using observational, longitudinal data in which treatment, candidate moderators, and possible confounders are time varying. The structural nested mean model (SNMM) is used to specify the moderated time-varying causal effects of interest in a conditional mean model for a continuous response given time-varying treatments and moderators. We present an easy-to-use estimator of the SNMM that combines an existing regression-with-residuals (RR) approach with an inverse-probability-of-treatment weighting (IPTW) strategy. The RR approach has been shown to identify the moderated time-varying causal effects if the time-varying moderators are also the sole time-varying confounders. The proposed IPTW+RR approach provides estimators of the moderated time-varying causal effects in the SNMM in the presence of an additional, auxiliary set of known and measured time-varying confounders. We use a small simulation experiment to compare IPTW+RR versus the traditional regression approach and to compare small and large sample properties of asymptotic versus bootstrap estimators of the standard errors for the IPTW+RR approach. This article clarifies the distinction between time-varying moderators and time-varying confounders. We illustrate the methodology in a case study to assess if time-varying substance use moderates treatment effects on future substance use. PMID:23873437

  16. Genetic parameters for body condition score, body weight, milk yield, and fertility estimated using random regression models.

    Science.gov (United States)

    Berry, D P; Buckley, F; Dillon, P; Evans, R D; Rath, M; Veerkamp, R F

    2003-11-01

    Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields from 8725 multiparous Holstein-Friesian cows. A cubic random regression was sufficient to model the changing genetic variances for BCS, BW, and milk across different days in milk. The genetic correlations between BCS and fertility changed little over the lactation; genetic correlations between BCS and interval to first service and between BCS and pregnancy rate to first service varied from -0.47 to -0.31, and from 0.15 to 0.38, respectively. This suggests that maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in midlactation when the genetic variance for BCS is largest. Selection for increased BW resulted in shorter intervals to first service, but more services and poorer pregnancy rates; genetic correlations between BW and pregnancy rate to first service varied from -0.52 to -0.45. Genetic selection for higher lactation milk yield alone through selection on increased milk yield in early lactation is likely to have a more deleterious effect on genetic merit for fertility than selection on higher milk yield in late lactation.

  17. Local food prices and their associations with children's weight and food security.

    Science.gov (United States)

    Morrissey, Taryn W; Jacknowitz, Alison; Vinopal, Katie

    2014-03-01

    Both obesity and food insecurity are important public health problems facing young children in the United States. A lack of affordable, healthy foods is one of the neighborhood factors presumed to underlie both food insecurity and obesity among children. We examine associations between local food prices and children's BMI, weight, and food security outcomes. We linked data from the Early Childhood Longitudinal Study-Birth Cohort, a nationally representative study of children from infancy to age 5, to local food price data from the Council for Community and Economic Research (C2ER) Cost-of-Living Index (n = 11,700 observations). Using ordinary least squares (OLS), linear probability, and within-child fixed effects (FE) models, we exploit the variability in food price data over time and among children who move residences focusing on a subsample of households under 300% of the Federal Poverty Level. Results from ordinary least squares and FE models indicate that higher-priced fruits and vegetables are associated with higher child BMI, and this relationship is driven by the prices of fresh (versus frozen or canned) fruits and vegetables. In the FE models, higher-priced soft drinks are associated with a lower likelihood of being overweight, and surprisingly, higher fast food prices are associated with a greater likelihood of being overweight. Policies that reduce the costs of fresh fruits and vegetables may be effective in promoting healthy weight outcomes among young children.

  18. The Spatial Distribution of Hepatitis C Virus Infections and Associated Determinants--An Application of a Geographically Weighted Poisson Regression for Evidence-Based Screening Interventions in Hotspots.

    Science.gov (United States)

    Kauhl, Boris; Heil, Jeanne; Hoebe, Christian J P A; Schweikart, Jürgen; Krafft, Thomas; Dukers-Muijrers, Nicole H T M

    2015-01-01

    Hepatitis C Virus (HCV) infections are a major cause for liver diseases. A large proportion of these infections remain hidden to care due to its mostly asymptomatic nature. Population-based screening and screening targeted on behavioural risk groups had not proven to be effective in revealing these hidden infections. Therefore, more practically applicable approaches to target screenings are necessary. Geographic Information Systems (GIS) and spatial epidemiological methods may provide a more feasible basis for screening interventions through the identification of hotspots as well as demographic and socio-economic determinants. Analysed data included all HCV tests (n = 23,800) performed in the southern area of the Netherlands between 2002-2008. HCV positivity was defined as a positive immunoblot or polymerase chain reaction test. Population data were matched to the geocoded HCV test data. The spatial scan statistic was applied to detect areas with elevated HCV risk. We applied global regression models to determine associations between population-based determinants and HCV risk. Geographically weighted Poisson regression models were then constructed to determine local differences of the association between HCV risk and population-based determinants. HCV prevalence varied geographically and clustered in urban areas. The main population at risk were middle-aged males, non-western immigrants and divorced persons. Socio-economic determinants consisted of one-person households, persons with low income and mean property value. However, the association between HCV risk and demographic as well as socio-economic determinants displayed strong regional and intra-urban differences. The detection of local hotspots in our study may serve as a basis for prioritization of areas for future targeted interventions. Demographic and socio-economic determinants associated with HCV risk show regional differences underlining that a one-size-fits-all approach even within small geographic

  19. Disability weights from a household survey in a low socio-economic setting: how does it compare to the global burden of disease 2010 study?

    Directory of Open Access Journals (Sweden)

    Ian Neethling

    2016-08-01

    Full Text Available Background: The global burden of disease (GBD 2010 study used a universal set of disability weights to estimate disability adjusted life years (DALYs by country. However, it is not clear whether these weights can be applied universally in calculating DALYs to inform local decision-making. This study derived disability weights for a resource-constrained community in Cape Town, South Africa, and interrogated whether the GBD 2010 disability weights necessarily represent the preferences of economically disadvantaged communities. Design: A household survey was conducted in Lavender Hill, Cape Town, to assess the health state preferences of the general public. The responses from a paired comparison valuation method were assessed using a probit regression. The probit coefficients were anchored onto the 0 to 1 disability weight scale by running a lowess regression on the GBD 2010 disability weights and interpolating the coefficients between the upper and lower limit of the smoothed disability weights. Results: Heroin and opioid dependence had the highest disability weight of 0.630, whereas intellectual disability had the lowest (0.040. Untreated injuries ranked higher than severe mental disorders. There were some counterintuitive results, such as moderate (15th and severe vision impairment (16th ranking higher than blindness (20th. A moderate correlation between the disability weights of the local study and those of the GBD 2010 study was observed (R2=0.440, p<0.05. This indicates that there was a relationship, although some conditions, such as untreated fracture of the radius or ulna, showed large variability in disability weights (0.488 in local study and 0.043 in GBD 2010. Conclusions: Respondents seemed to value physical mobility higher than cognitive functioning, which is in contrast to the GBD 2010 study. This study shows that not all health state preferences are universal. Studies estimating DALYs need to derive local disability weights using

  20. Genetic parameters for quail body weights using a random ...

    African Journals Online (AJOL)

    A model including fixed and random linear regressions is described for analyzing body weights at different ages. In this study, (co)variance components, heritabilities for quail weekly weights and genetic correlations among these weights were estimated using a random regression model by DFREML under DXMRR option.

  1. Weighted semiconvex spaces of measurable functions

    International Nuclear Information System (INIS)

    Olaleru, J.O.

    2001-12-01

    Semiconvex spaces are intermediates between locally convex spaces and the non locally convex topological vector spaces. They include all locally convex spaces; hence it is a generalization of locally convex spaces. In this article, we make a study of weighted semiconvex spaces parallel to weighted locally convex spaces where continuous functions are replaced with measurable functions and N p family replaces Nachbin family on a locally compact space X. Among others, we examine the Hausdorffness, completeness, inductive limits, barrelledness and countably barrelledness of weighted semiconvex spaces. New results are obtained while we have a more elegant proofs of old results. Furthermore, we get extensions of some of the old results. It is observed that the technique of proving theorems in weighted locally convex spaces can be adapted to that of weighted semicovex spaces of measurable functions in most cases. (author)

  2. Growth curves of preschool children in the northeast of iran: a population based study using quantile regression approach.

    Science.gov (United States)

    Payande, Abolfazl; Tabesh, Hamed; Shakeri, Mohammad Taghi; Saki, Azadeh; Safarian, Mohammad

    2013-01-14

    Growth charts are widely used to assess children's growth status and can provide a trajectory of growth during early important months of life. The objectives of this study are going to construct growth charts and normal values of weight-for-age for children aged 0 to 5 years using a powerful and applicable methodology. The results compare with the World Health Organization (WHO) references and semi-parametric LMS method of Cole and Green. A total of 70737 apparently healthy boys and girls aged 0 to 5 years were recruited in July 2004 for 20 days from those attending community clinics for routine health checks as a part of a national survey. Anthropometric measurements were done by trained health staff using WHO methodology. The nonparametric quantile regression method obtained by local constant kernel estimation of conditional quantiles curves using for estimation of curves and normal values. The weight-for-age growth curves for boys and girls aged from 0 to 5 years were derived utilizing a population of children living in the northeast of Iran. The results were similar to the ones obtained by the semi-parametric LMS method in the same data. Among all age groups from 0 to 5 years, the median values of children's weight living in the northeast of Iran were lower than the corresponding values in WHO reference data. The weight curves of boys were higher than those of girls in all age groups. The differences between growth patterns of children living in the northeast of Iran versus international ones necessitate using local and regional growth charts. International normal values may not properly recognize the populations at risk for growth problems in Iranian children. Quantile regression (QR) as a flexible method which doesn't require restricted assumptions, proposed for estimation reference curves and normal values.

  3. Vasoconstriction Potency Induced by Aminoamide Local Anesthetics Correlates with Lipid Solubility

    Directory of Open Access Journals (Sweden)

    Hui-Jin Sung

    2012-01-01

    Full Text Available Aminoamide local anesthetics induce vasoconstriction in vivo and in vitro. The goals of this in vitro study were to investigate the potency of local anesthetic-induced vasoconstriction and to identify the physicochemical property (octanol/buffer partition coefficient, pKa, molecular weight, or potency of local anesthetics that determines their potency in inducing isolated rat aortic ring contraction. Cumulative concentration-response curves to local anesthetics (levobupivacaine, ropivacaine, lidocaine, and mepivacaine were obtained from isolated rat aorta. Regression analyses were performed to determine the relationship between the reported physicochemical properties of local anesthetics and the local anesthetic concentration that produced 50% (ED50 of the local anesthetic-induced maximum vasoconstriction. We determined the order of potency (ED50 of vasoconstriction among local anesthetics to be levobupivacaine > ropivacaine > lidocaine > mepivacaine. The relative importance of the independent variables that affect the vasoconstriction potency is octanol/buffer partition coefficient > potency > pKa > molecular weight. The ED50 in endothelium-denuded aorta negatively correlated with the octanol/buffer partition coefficient of local anesthetics (r2=0.9563; P<0.001. The potency of the vasoconstriction in the endothelium-denuded aorta induced by local anesthetics is determined primarily by lipid solubility and, in part, by other physicochemical properties including potency and pKa.

  4. Scrub typhus islands in the Taiwan area and the association between scrub typhus disease and forest land use and farmer population density: geographically weighted regression

    Science.gov (United States)

    2013-01-01

    Background The Taiwan area comprises the main island of Taiwan and several small islands located off the coast of the Southern China. The eastern two-thirds of Taiwan are characterized by rugged mountains covered with tropical and subtropical vegetation. The western region of Taiwan is characterized by flat or gently rolling plains. Geographically, the Taiwan area is diverse in ecology and environment, although scrub typhus threatens local human populations. In this study, we investigate the effects of seasonal and meteorological factors on the incidence of scrub typhus infection among 10 local climate regions. The correlation between the spatial distribution of scrub typhus and cultivated forests in Taiwan, as well as the relationship between scrub typhus incidence and the population density of farm workers is examined. Methods We applied Pearson’s product moment correlation to calculate the correlation between the incidence of scrub typhus and meteorological factors among 10 local climate regions. We used the geographically weighted regression (GWR) method, a type of spatial regression that generates parameters disaggregated by the spatial units of analysis, to detail and map each regression point for the response variables of the standardized incidence ratio (SIR)-district scrub typhus. We also applied the GWR to examine the explanatory variables of types of forest-land use and farm worker density in Taiwan in 2005. Results In the Taiwan Area, scrub typhus endemic areas are located in the southeastern regions and mountainous townships of Taiwan, as well as the Pescadore, Kinmen, and Matou Islands. Among these islands and low-incidence areas in the central western and southwestern regions of Taiwan, we observed a significant correlation between scrub typhus incidence and surface temperature. No similar significant correlation was found in the endemic areas (e.g., the southeastern region and the mountainous area of Taiwan). Precipitation correlates positively

  5. Short-term load forecasting with increment regression tree

    Energy Technology Data Exchange (ETDEWEB)

    Yang, Jingfei; Stenzel, Juergen [Darmstadt University of Techonology, Darmstadt 64283 (Germany)

    2006-06-15

    This paper presents a new regression tree method for short-term load forecasting. Both increment and non-increment tree are built according to the historical data to provide the data space partition and input variable selection. Support vector machine is employed to the samples of regression tree nodes for further fine regression. Results of different tree nodes are integrated through weighted average method to obtain the comprehensive forecasting result. The effectiveness of the proposed method is demonstrated through its application to an actual system. (author)

  6. Robust extraction of baseline signal of atmospheric trace species using local regression

    Science.gov (United States)

    Ruckstuhl, A. F.; Henne, S.; Reimann, S.; Steinbacher, M.; Vollmer, M. K.; O'Doherty, S.; Buchmann, B.; Hueglin, C.

    2012-11-01

    The identification of atmospheric trace species measurements that are representative of well-mixed background air masses is required for monitoring atmospheric composition change at background sites. We present a statistical method based on robust local regression that is well suited for the selection of background measurements and the estimation of associated baseline curves. The bootstrap technique is applied to calculate the uncertainty in the resulting baseline curve. The non-parametric nature of the proposed approach makes it a very flexible data filtering method. Application to carbon monoxide (CO) measured from 1996 to 2009 at the high-alpine site Jungfraujoch (Switzerland, 3580 m a.s.l.), and to measurements of 1,1-difluoroethane (HFC-152a) from Jungfraujoch (2000 to 2009) and Mace Head (Ireland, 1995 to 2009) demonstrates the feasibility and usefulness of the proposed approach. The determined average annual change of CO at Jungfraujoch for the 1996 to 2009 period as estimated from filtered annual mean CO concentrations is -2.2 ± 1.1 ppb yr-1. For comparison, the linear trend of unfiltered CO measurements at Jungfraujoch for this time period is -2.9 ± 1.3 ppb yr-1.

  7. Ordinary Least Squares and Quantile Regression: An Inquiry-Based Learning Approach to a Comparison of Regression Methods

    Science.gov (United States)

    Helmreich, James E.; Krog, K. Peter

    2018-01-01

    We present a short, inquiry-based learning course on concepts and methods underlying ordinary least squares (OLS), least absolute deviation (LAD), and quantile regression (QR). Students investigate squared, absolute, and weighted absolute distance functions (metrics) as location measures. Using differential calculus and properties of convex…

  8. Weight Rhythms: Weight Increases during Weekends and Decreases during Weekdays

    Directory of Open Access Journals (Sweden)

    Anna-Leena Orsama

    2014-01-01

    Full Text Available Background/Aims: The week's cycle influences sleep, exercise, and eating habits. An accurate description of weekly weight rhythms has not been reported yet - especially across people who lose weight versus those who maintain or gain weight. Methods: The daily weight in 80 adults (BMI 20.0-33.5 kg/m2; age, 25-62 years was recorded and analysed to determine if a group-level weekly weight fluctuation exists. This was a retrospective study of 4,657 measurements during 15-330 monitoring days. Semi-parametric regression was used to model the rhythm. Results: A pattern of daily weight changes was found (p Conclusion: Weight variations between weekends and weekdays should be considered as normal instead of signs of weight gain. Those who compensate the most are most likely to either lose or maintain weight over time. Long-term habits may make more of a difference than short-term splurges. People prone to weight gain could be counselled about the importance of weekday compensation.

  9. Marital status and body weight, weight perception, and weight management among U.S. adults.

    Science.gov (United States)

    Klos, Lori A; Sobal, Jeffery

    2013-12-01

    Married individuals often have higher body weights than unmarried individuals, but it is unclear how marital roles affect body weight-related perceptions, desires, and behaviors. This study analyzed cross-sectional data for 4,089 adult men and 3,989 adult women using multinomial logistic regression to examine associations between marital status, perceived body weight, desired body weight, and weight management approach. Controlling for demographics and current weight, married or cohabiting women and divorced or separated women more often perceived themselves as overweight and desired to weigh less than women who had never married. Marital status was unrelated to men's weight perception and desired weight change. Marital status was also generally unrelated to weight management approach, except that divorced or separated women were more likely to have intentionally lost weight within the past year compared to never married women. Additionally, never married men were more likely to be attempting to prevent weight gain than married or cohabiting men and widowed men. Overall, married and formerly married women more often perceived themselves as overweight and desired a lower weight. Men's marital status was generally unassociated with weight-related perceptions, desires, and behaviors. Women's but not men's marital roles appear to influence their perceived and desired weight, suggesting that weight management interventions should be sensitive to both marital status and gender differences. © 2013 Elsevier Ltd. All rights reserved.

  10. Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach

    OpenAIRE

    Zhihua Zhang; Rachel J. C. Chen; Lee D. Han; Lu Yang

    2017-01-01

    Airbnb has been increasingly gaining popularity since 2008 due to its low prices and direct interactions with the local community. This paper employed a general linear model (GLM) and a geographically weighted regression (GWR) model to identify the key factors affecting Airbnb listing prices using data sets of 794 samples of Airbnb listings of business units in Metro Nashville, Tennessee. The results showed that the GWR model performs better than the GLM in terms of accuracy and affected vari...

  11. Importance weighting of local flux measurements to improve reactivity predictions in nuclear systems

    Energy Technology Data Exchange (ETDEWEB)

    Dulla, Sandra; Hoh, Siew Sin; Nervo, Marta; Ravetto, Piero [Politecnico di Torino, Dipt. Energia (Italy)

    2015-07-15

    The reactivity monitoring is a key aspect for the safe operation of nuclear reactors, especially for subcritical source-driven systems. Various methods are available for both, off-line and on-line reactivity determination from direct measurements carried out on the reactor. Usually the methods are based on the inverse point kinetic model applied to signals from neutron detectors and results may be severely affected by space and spectral effects. Such effects need to be compensated and correction procedures have to be applied. In this work, a new approach is proposed, by using the full information from different local measurements to generate a global signal through a proper weighting of the signals provided by single neutron detectors. A weighting techique based on the use of the adjoint flux proves to be efficient in improving the prediction capability of inverse techniques. The idea is applied to the recently developed algorithm, named MAρTA, that can be used in both off-line and online modes.

  12. Evaluation of Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) for Water Quality Monitoring: A Case Study for the Estimation of Salinity

    Science.gov (United States)

    Nazeer, Majid; Bilal, Muhammad

    2018-04-01

    Landsat-5 Thematic Mapper (TM) dataset have been used to estimate salinity in the coastal area of Hong Kong. Four adjacent Landsat TM images were used in this study, which was atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer code. The atmospherically corrected images were further used to develop models for salinity using Ordinary Least Square (OLS) regression and Geographically Weighted Regression (GWR) based on in situ data of October 2009. Results show that the coefficient of determination ( R 2) of 0.42 between the OLS estimated and in situ measured salinity is much lower than that of the GWR model, which is two times higher ( R 2 = 0.86). It indicates that the GWR model has more ability than the OLS regression model to predict salinity and show its spatial heterogeneity better. It was observed that the salinity was high in Deep Bay (north-western part of Hong Kong) which might be due to the industrial waste disposal, whereas the salinity was estimated to be constant (32 practical salinity units) towards the open sea.

  13. General regression and representation model for classification.

    Directory of Open Access Journals (Sweden)

    Jianjun Qian

    Full Text Available Recently, the regularized coding-based classification methods (e.g. SRC and CRC show a great potential for pattern classification. However, most existing coding methods assume that the representation residuals are uncorrelated. In real-world applications, this assumption does not hold. In this paper, we take account of the correlations of the representation residuals and develop a general regression and representation model (GRR for classification. GRR not only has advantages of CRC, but also takes full use of the prior information (e.g. the correlations between representation residuals and representation coefficients and the specific information (weight matrix of image pixels to enhance the classification performance. GRR uses the generalized Tikhonov regularization and K Nearest Neighbors to learn the prior information from the training data. Meanwhile, the specific information is obtained by using an iterative algorithm to update the feature (or image pixel weights of the test sample. With the proposed model as a platform, we design two classifiers: basic general regression and representation classifier (B-GRR and robust general regression and representation classifier (R-GRR. The experimental results demonstrate the performance advantages of proposed methods over state-of-the-art algorithms.

  14. Robust extraction of baseline signal of atmospheric trace species using local regression

    Directory of Open Access Journals (Sweden)

    A. F. Ruckstuhl

    2012-11-01

    Full Text Available The identification of atmospheric trace species measurements that are representative of well-mixed background air masses is required for monitoring atmospheric composition change at background sites. We present a statistical method based on robust local regression that is well suited for the selection of background measurements and the estimation of associated baseline curves. The bootstrap technique is applied to calculate the uncertainty in the resulting baseline curve. The non-parametric nature of the proposed approach makes it a very flexible data filtering method. Application to carbon monoxide (CO measured from 1996 to 2009 at the high-alpine site Jungfraujoch (Switzerland, 3580 m a.s.l., and to measurements of 1,1-difluoroethane (HFC-152a from Jungfraujoch (2000 to 2009 and Mace Head (Ireland, 1995 to 2009 demonstrates the feasibility and usefulness of the proposed approach.

    The determined average annual change of CO at Jungfraujoch for the 1996 to 2009 period as estimated from filtered annual mean CO concentrations is −2.2 ± 1.1 ppb yr−1. For comparison, the linear trend of unfiltered CO measurements at Jungfraujoch for this time period is −2.9 ± 1.3 ppb yr−1.

  15. Calorie Labeling in Chain Restaurants and Body Weight: Evidence from New York.

    Science.gov (United States)

    Restrepo, Brandon J

    2017-10-01

    This study analyzes the impact of local mandatory calorie labeling laws implemented by New York jurisdictions on body weight. The analysis indicates that on average the point-of-purchase provision of calorie information on chain restaurant menus reduced body mass index (BMI) by 1.5% and lowered the risk of obesity by 12%. Quantile regression results indicate that calorie labeling has similar impacts across the BMI distribution. An analysis of heterogeneity suggests that calorie labeling has a larger impact on the body weight of lower income individuals, especially lower income minorities. The estimated impacts of calorie labeling on physical activity, smoking, and the consumption of alcoholic beverages, fruits, and vegetables are small in magnitude, which suggests that other margins of adjustment drive the body-weight impacts estimated here. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  16. Reconstruction of Local Sea Levels at South West Pacific Islands—A Multiple Linear Regression Approach (1988-2014)

    Science.gov (United States)

    Kumar, V.; Melet, A.; Meyssignac, B.; Ganachaud, A.; Kessler, W. S.; Singh, A.; Aucan, J.

    2018-02-01

    Rising sea levels are a critical concern in small island nations. The problem is especially serious in the western south Pacific, where the total sea level rise over the last 60 years has been up to 3 times the global average. In this study, we aim at reconstructing sea levels at selected sites in the region (Suva, Lautoka—Fiji, and Nouméa—New Caledonia) as a multilinear regression (MLR) of atmospheric and oceanic variables. We focus on sea level variability at interannual-to-interdecadal time scales, and trend over the 1988-2014 period. Local sea levels are first expressed as a sum of steric and mass changes. Then a dynamical approach is used based on wind stress curl as a proxy for the thermosteric component, as wind stress curl anomalies can modulate the thermocline depth and resultant sea levels via Rossby wave propagation. Statistically significant predictors among wind stress curl, halosteric sea level, zonal/meridional wind stress components, and sea surface temperature are used to construct a MLR model simulating local sea levels. Although we are focusing on the local scale, the global mean sea level needs to be adjusted for. Our reconstructions provide insights on key drivers of sea level variability at the selected sites, showing that while local dynamics and the global signal modulate sea level to a given extent, most of the variance is driven by regional factors. On average, the MLR model is able to reproduce 82% of the variance in island sea level, and could be used to derive local sea level projections via downscaling of climate models.

  17. Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models

    Directory of Open Access Journals (Sweden)

    Chuanglin Fang

    2015-11-01

    Full Text Available Urban air pollution is one of the most visible environmental problems to have accompanied China’s rapid urbanization. Based on emission inventory data from 2014, gathered from 289 cities, we used Global and Local Moran’s I to measure the spatial autorrelation of Air Quality Index (AQI values at the city level, and employed Ordinary Least Squares (OLS, Spatial Lag Model (SAR, and Geographically Weighted Regression (GWR to quantitatively estimate the comprehensive impact and spatial variations of China’s urbanization process on air quality. The results show that a significant spatial dependence and heterogeneity existed in AQI values. Regression models revealed urbanization has played an important negative role in determining air quality in Chinese cities. The population, urbanization rate, automobile density, and the proportion of secondary industry were all found to have had a significant influence over air quality. Per capita Gross Domestic Product (GDP and the scale of urban land use, however, failed the significance test at 10% level. The GWR model performed better than global models and the results of GWR modeling show that the relationship between urbanization and air quality was not constant in space. Further, the local parameter estimates suggest significant spatial variation in the impacts of various urbanization factors on air quality.

  18. Stochastic search, optimization and regression with energy applications

    Science.gov (United States)

    Hannah, Lauren A.

    models. We evaluate DP-GLM on several data sets, comparing it to modern methods of nonparametric regression like CART, Bayesian trees and Gaussian processes. Compared to existing techniques, the DP-GLM provides a single model (and corresponding inference algorithms) that performs well in many regression settings. Finally, we study convex stochastic search problems where a noisy objective function value is observed after a decision is made. There are many stochastic search problems whose behavior depends on an exogenous state variable which affects the shape of the objective function. Currently, there is no general purpose algorithm to solve this class of problems. We use nonparametric density estimation to take observations from the joint state-outcome distribution and use them to infer the optimal decision for a given query state. We propose two solution methods that depend on the problem characteristics: function-based and gradient-based optimization. We examine two weighting schemes, kernel-based weights and Dirichlet process-based weights, for use with the solution methods. The weights and solution methods are tested on a synthetic multi-product newsvendor problem and the hour-ahead wind commitment problem. Our results show that in some cases Dirichlet process weights offer substantial benefits over kernel based weights and more generally that nonparametric estimation methods provide good solutions to otherwise intractable problems.

  19. LINEAR REGRESSION MODEL ESTİMATİON FOR RIGHT CENSORED DATA

    Directory of Open Access Journals (Sweden)

    Ersin Yılmaz

    2016-05-01

    Full Text Available In this study, firstly we will define a right censored data. If we say shortly right-censored data is censoring values that above the exact line. This may be related with scaling device. And then  we will use response variable acquainted from right-censored explanatory variables. Then the linear regression model will be estimated. For censored data’s existence, Kaplan-Meier weights will be used for  the estimation of the model. With the weights regression model  will be consistent and unbiased with that.   And also there is a method for the censored data that is a semi parametric regression and this method also give  useful results  for censored data too. This study also might be useful for the health studies because of the censored data used in medical issues generally.

  20. Alternative regression models to assess increase in childhood BMI.

    Science.gov (United States)

    Beyerlein, Andreas; Fahrmeir, Ludwig; Mansmann, Ulrich; Toschke, André M

    2008-09-08

    Body mass index (BMI) data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs), quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS). We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.

  1. Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression.

    Science.gov (United States)

    Chen, Qiang; Mei, Kun; Dahlgren, Randy A; Wang, Ting; Gong, Jian; Zhang, Minghua

    2016-12-01

    As an important regulator of pollutants in overland flow and interflow, land use has become an essential research component for determining the relationships between surface water quality and pollution sources. This study investigated the use of ordinary least squares (OLS) and geographically weighted regression (GWR) models to identify the impact of land use and population density on surface water quality in the Wen-Rui Tang River watershed of eastern China. A manual variable excluding-selecting method was explored to resolve multicollinearity issues. Standard regression coefficient analysis coupled with cluster analysis was introduced to determine which variable had the greatest influence on water quality. Results showed that: (1) Impact of land use on water quality varied with spatial and seasonal scales. Both positive and negative effects for certain land-use indicators were found in different subcatchments. (2) Urban land was the dominant factor influencing N, P and chemical oxygen demand (COD) in highly urbanized regions, but the relationship was weak as the pollutants were mainly from point sources. Agricultural land was the primary factor influencing N and P in suburban and rural areas; the relationship was strong as the pollutants were mainly from agricultural surface runoff. Subcatchments located in suburban areas were identified with urban land as the primary influencing factor during the wet season while agricultural land was identified as a more prevalent influencing factor during the dry season. (3) Adjusted R 2 values in OLS models using the manual variable excluding-selecting method averaged 14.3% higher than using stepwise multiple linear regressions. However, the corresponding GWR models had adjusted R 2 ~59.2% higher than the optimal OLS models, confirming that GWR models demonstrated better prediction accuracy. Based on our findings, water resource protection policies should consider site-specific land-use conditions within each watershed to

  2. Risk factors for low birth weight according to the multiple logistic regression model. A retrospective cohort study in José María Morelos municipality, Quintana Roo, Mexico.

    Science.gov (United States)

    Franco Monsreal, José; Tun Cobos, Miriam Del Ruby; Hernández Gómez, José Ricardo; Serralta Peraza, Lidia Esther Del Socorro

    2018-01-17

    Low birth weight has been an enigma for science over time. There have been many researches on its causes and its effects. Low birth weight is an indicator that predicts the probability of a child surviving. In fact, there is an exponential relationship between weight deficit, gestational age, and perinatal mortality. Multiple logistic regression is one of the most expressive and versatile statistical instruments available for the analysis of data in both clinical and epidemiology settings, as well as in public health. To assess in a multivariate fashion the importance of 17 independent variables in low birth weight (dependent variable) of children born in the Mayan municipality of José María Morelos, Quintana Roo, Mexico. Analytical observational epidemiological cohort study with retrospective temporality. Births that met the inclusion criteria occurred in the "Hospital Integral Jose Maria Morelos" of the Ministry of Health corresponding to the Maya municipality of Jose Maria Morelos during the period from August 1, 2014 to July 31, 2015. The total number of newborns recorded was 1,147; 84 of which (7.32%) had low birth weight. To estimate the independent association between the explanatory variables (potential risk factors) and the response variable, a multiple logistic regression analysis was performed using the IBM SPSS Statistics 22 software. In ascending numerical order values of odds ratio > 1 indicated the positive contribution of explanatory variables or possible risk factors: "unmarried" marital status (1.076, 95% confidence interval: 0.550 to 2.104); age at menarche ≤ 12 years (1.08, 95% confidence interval: 0.64 to 1.84); history of abortion(s) (1.14, 95% confidence interval: 0.44 to 2.93); maternal weight < 50 kg (1.51, 95% confidence interval: 0.83 to 2.76); number of prenatal consultations ≤ 5 (1.86, 95% confidence interval: 0.94 to 3.66); maternal age ≥ 36 years (3.5, 95% confidence interval: 0.40 to 30.47); maternal age ≤ 19 years (3

  3. mPLR-Loc: an adaptive decision multi-label classifier based on penalized logistic regression for protein subcellular localization prediction.

    Science.gov (United States)

    Wan, Shibiao; Mak, Man-Wai; Kung, Sun-Yuan

    2015-03-15

    Proteins located in appropriate cellular compartments are of paramount importance to exert their biological functions. Prediction of protein subcellular localization by computational methods is required in the post-genomic era. Recent studies have been focusing on predicting not only single-location proteins but also multi-location proteins. However, most of the existing predictors are far from effective for tackling the challenges of multi-label proteins. This article proposes an efficient multi-label predictor, namely mPLR-Loc, based on penalized logistic regression and adaptive decisions for predicting both single- and multi-location proteins. Specifically, for each query protein, mPLR-Loc exploits the information from the Gene Ontology (GO) database by using its accession number (AC) or the ACs of its homologs obtained via BLAST. The frequencies of GO occurrences are used to construct feature vectors, which are then classified by an adaptive decision-based multi-label penalized logistic regression classifier. Experimental results based on two recent stringent benchmark datasets (virus and plant) show that mPLR-Loc remarkably outperforms existing state-of-the-art multi-label predictors. In addition to being able to rapidly and accurately predict subcellular localization of single- and multi-label proteins, mPLR-Loc can also provide probabilistic confidence scores for the prediction decisions. For readers' convenience, the mPLR-Loc server is available online (http://bioinfo.eie.polyu.edu.hk/mPLRLocServer). Copyright © 2014 Elsevier Inc. All rights reserved.

  4. Perceived Physician-informed Weight Status Predicts Accurate Weight Self-Perception and Weight Self-Regulation in Low-income, African American Women.

    Science.gov (United States)

    Harris, Charlie L; Strayhorn, Gregory; Moore, Sandra; Goldman, Brian; Martin, Michelle Y

    2016-01-01

    Obese African American women under-appraise their body mass index (BMI) classification and report fewer weight loss attempts than women who accurately appraise their weight status. This cross-sectional study examined whether physician-informed weight status could predict weight self-perception and weight self-regulation strategies in obese women. A convenience sample of 118 low-income women completed a survey assessing demographic characteristics, comorbidities, weight self-perception, and weight self-regulation strategies. BMI was calculated during nurse triage. Binary logistic regression models were performed to test hypotheses. The odds of obese accurate appraisers having been informed about their weight status were six times greater than those of under-appraisers. The odds of those using an "approach" self-regulation strategy having been physician-informed were four times greater compared with those using an "avoidance" strategy. Physicians are uniquely positioned to influence accurate weight self-perception and adaptive weight self-regulation strategies in underserved women, reducing their risk for obesity-related morbidity.

  5. Quantification of endocrine disruptors and pesticides in water by gas chromatography-tandem mass spectrometry. Method validation using weighted linear regression schemes.

    Science.gov (United States)

    Mansilha, C; Melo, A; Rebelo, H; Ferreira, I M P L V O; Pinho, O; Domingues, V; Pinho, C; Gameiro, P

    2010-10-22

    A multi-residue methodology based on a solid phase extraction followed by gas chromatography-tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC-MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness. Copyright © 2010 Elsevier B.V. All rights reserved.

  6. Alternative regression models to assess increase in childhood BMI

    Directory of Open Access Journals (Sweden)

    Mansmann Ulrich

    2008-09-01

    Full Text Available Abstract Background Body mass index (BMI data usually have skewed distributions, for which common statistical modeling approaches such as simple linear or logistic regression have limitations. Methods Different regression approaches to predict childhood BMI by goodness-of-fit measures and means of interpretation were compared including generalized linear models (GLMs, quantile regression and Generalized Additive Models for Location, Scale and Shape (GAMLSS. We analyzed data of 4967 children participating in the school entry health examination in Bavaria, Germany, from 2001 to 2002. TV watching, meal frequency, breastfeeding, smoking in pregnancy, maternal obesity, parental social class and weight gain in the first 2 years of life were considered as risk factors for obesity. Results GAMLSS showed a much better fit regarding the estimation of risk factors effects on transformed and untransformed BMI data than common GLMs with respect to the generalized Akaike information criterion. In comparison with GAMLSS, quantile regression allowed for additional interpretation of prespecified distribution quantiles, such as quantiles referring to overweight or obesity. The variables TV watching, maternal BMI and weight gain in the first 2 years were directly, and meal frequency was inversely significantly associated with body composition in any model type examined. In contrast, smoking in pregnancy was not directly, and breastfeeding and parental social class were not inversely significantly associated with body composition in GLM models, but in GAMLSS and partly in quantile regression models. Risk factor specific BMI percentile curves could be estimated from GAMLSS and quantile regression models. Conclusion GAMLSS and quantile regression seem to be more appropriate than common GLMs for risk factor modeling of BMI data.

  7. Gas Source Localization via Behaviour Based Mobile Robot and Weighted Arithmetic Mean

    Science.gov (United States)

    Yeon, Ahmad Shakaff Ali; Kamarudin, Kamarulzaman; Visvanathan, Retnam; Mamduh Syed Zakaria, Syed Muhammad; Zakaria, Ammar; Munirah Kamarudin, Latifah

    2018-03-01

    This work is concerned with the localization of gas source in dynamic indoor environment using a single mobile robot system. Algorithms such as Braitenberg, Zig-Zag and the combination of the two were implemented on the mobile robot as gas plume searching and tracing behaviours. To calculate the gas source location, a weighted arithmetic mean strategy was used. All experiments were done on an experimental testbed consisting of a large gas sensor array (LGSA) to monitor real-time gas concentration within the testbed. Ethanol gas was released within the testbed and the source location was marked using a pattern that can be tracked by a pattern tracking system. A pattern template was also mounted on the mobile robot to track the trajectory of the mobile robot. Measurements taken by the mobile robot and the LGSA were then compared to verify the experiments. A combined total of 36.5 hours of real time experimental runs were done and the typical results from such experiments were presented in this paper. From the results, we obtained gas source localization errors between 0.4m to 1.2m from the real source location.

  8. Local patient dose diagnostic reference levels in pediatric interventional cardiology in Chile using age bands and patient weight values.

    Science.gov (United States)

    Ubeda, Carlos; Miranda, Patricia; Vano, Eliseo

    2015-02-01

    To present the results of a patient dose evaluation program in pediatric cardiology and propose local diagnostic reference levels (DRLs) for different types of procedure and age range, in addition to suggesting approaches to correlate patient dose values with patient weight. This study was the first conducted in Latin America for pediatric interventional cardiology under the auspices of the International Atomic Energy Agency. Over three years, the following data regarding demographic and patient dose values were collected: age, gender, weight, height, number of cine series, total number of cine frames, fluoroscopy time (FT), and two dosimetric quantities, dose-area product (DAP) and cumulative dose (CD), at the patient entrance reference point. The third quartile values for FT, DAP, CD, number of cine series, and the DAP/body weight ratio were proposed as the set of quantities to use as local DRLs. Five hundred and seventeen patients were divided into four age groups. Sample sizes by age group were 120 for bands used in Europe, complemented with the values of the ratio between DAP and patient weight. This permits a rough estimate of DRLs for different patient weights and the refining of these values for the age bands when there may be large differences in child size. These DRLs were obtained at the largest pediatric hospital in Chile, with an active optimization program, and could be used by other hospitals in the Latin America region to compare their current patient dose values and determine whether corrective action is appropriate. © 2015 American Association of Physicists in Medicine.

  9. Preventing Weight Gain

    Science.gov (United States)

    ... Local Programs Related Topics Diabetes Nutrition Preventing Weight Gain Language: English (US) Español (Spanish) Recommend on Facebook ... cancer. Choosing an Eating Plan to Prevent Weight Gain So, how do you choose a healthful eating ...

  10. LENGTH-WEIGHT RELATIONSHIP AND CONDITION FACTOR OF ...

    African Journals Online (AJOL)

    Data Collection and Analysis. The measurements of length (cm), weight (g) and the condition factor of individual fish sampled were recorded. The relationship between length and weight of the fish was examined by simple linear regression using WINKS software. The variations in the length-weight represented by 'b' were.

  11. Correlates of Low Birth Weight

    Directory of Open Access Journals (Sweden)

    Ankur Barua MD, PhD

    2014-12-01

    Full Text Available Background. Low birth weight is the single most important factor that determines the chances of child survival. A recent annual estimation indicated that nearly 8 million infants are born with low birth weight in India. The infant mortality rate is about 20 times greater for all low birth weight babies. Methods. A matched case–control study was conducted on 130 low birth weight babies and 130 controls for 12 months (from August 1, 2007, to July 31, 2008 at the Central Referral Hospital, Tadong, East District of Sikkim, India. Data were analyzed using the Statistical Package for Social Sciences, version 10.0 for Windows. Chi-square test and multiple logistic regression were applied. A P value less than .05 was considered as significant. Results. In the first phase of this study, 711 newborn babies, borne by 680 mothers, were screened at the Central Referral Hospital of Sikkim during the 1-year study period, and the proportion of low birth weight babies was determined to be 130 (18.3%. Conclusion. Multiple logistic regression analysis, conducted in the second phase, revealed that low or middle socioeconomic status, maternal underweight, twin pregnancy, previous history of delivery of low birth weight babies, smoking and consumption of alcohol during pregnancy, and congenital anomalies had independent significant association with low birth weight in this study population.

  12. Attribution of weight regain to emotional reasons amongst European adults with overweight and obesity who regained weight following a weight loss attempt

    DEFF Research Database (Denmark)

    Sainsbury, Kirby; Evans, Elizabeth; Pedersen, Susanne

    2018-01-01

    Purpose: Despite the wide availability of effective weight loss programmes, maintenance of weight loss remains challenging. Difficulties in emotion regulation are associated with binge eating and may represent one barrier to long-term intervention effectiveness in obesity. The purpose of this study...... was to determine the relationship between emotion regulation difficulties and the extent of weight regain in a sample of adults who had lost, and then regained, weight, and to examine the character-istics associated with emotional difficulties. Methods: 2000 adults from three European countries (UK, Portugal...... for emotion regulation difficulties). Spearman’s correlations and logistic regression were used to assess the associa-tions between emotion regulation, weight regain, and strategy use. Results: Emotion regulation difficulties were associated with greater weight regain (N= 1594 who lost and regained weight...

  13. H0 from cosmic chronometers and Type Ia supernovae, with Gaussian Processes and the novel Weighted Polynomial Regression method

    Science.gov (United States)

    Gómez-Valent, Adrià; Amendola, Luca

    2018-04-01

    In this paper we present new constraints on the Hubble parameter H0 using: (i) the available data on H(z) obtained from cosmic chronometers (CCH); (ii) the Hubble rate data points extracted from the supernovae of Type Ia (SnIa) of the Pantheon compilation and the Hubble Space Telescope (HST) CANDELS and CLASH Multy-Cycle Treasury (MCT) programs; and (iii) the local HST measurement of H0 provided by Riess et al. (2018), H0HST=(73.45±1.66) km/s/Mpc. Various determinations of H0 using the Gaussian processes (GPs) method and the most updated list of CCH data have been recently provided by Yu, Ratra & Wang (2018). Using the Gaussian kernel they find H0=(67.42± 4.75) km/s/Mpc. Here we extend their analysis to also include the most released and complete set of SnIa data, which allows us to reduce the uncertainty by a factor ~ 3 with respect to the result found by only considering the CCH information. We obtain H0=(67.06± 1.68) km/s/Mpc, which favors again the lower range of values for H0 and is in tension with H0HST. The tension reaches the 2.71σ level. We round off the GPs determination too by taking also into account the error propagation of the kernel hyperparameters when the CCH with and without H0HST are used in the analysis. In addition, we present a novel method to reconstruct functions from data, which consists in a weighted sum of polynomial regressions (WPR). We apply it from a cosmographic perspective to reconstruct H(z) and estimate H0 from CCH and SnIa measurements. The result obtained with this method, H0=(68.90± 1.96) km/s/Mpc, is fully compatible with the GPs ones. Finally, a more conservative GPs+WPR value is also provided, H0=(68.45± 2.00) km/s/Mpc, which is still almost 2σ away from H0HST.

  14. Forward-weighted CADIS method for variance reduction of Monte Carlo calculations of distributions and multiple localized quantities

    International Nuclear Information System (INIS)

    Wagner, J. C.; Blakeman, E. D.; Peplow, D. E.

    2009-01-01

    This paper presents a new hybrid (Monte Carlo/deterministic) method for increasing the efficiency of Monte Carlo calculations of distributions, such as flux or dose rate distributions (e.g., mesh tallies), as well as responses at multiple localized detectors and spectra. This method, referred to as Forward-Weighted CADIS (FW-CADIS), is a variation on the Consistent Adjoint Driven Importance Sampling (CADIS) method, which has been used for some time to very effectively improve the efficiency of Monte Carlo calculations of localized quantities, e.g., flux, dose, or reaction rate at a specific location. The basis of this method is the development of an importance function that represents the importance of particles to the objective of uniform Monte Carlo particle density in the desired tally regions. Implementation of this method utilizes the results from a forward deterministic calculation to develop a forward-weighted source for a deterministic adjoint calculation. The resulting adjoint function is then used to generate consistent space- and energy-dependent source biasing parameters and weight windows that are used in a forward Monte Carlo calculation to obtain approximately uniform statistical uncertainties in the desired tally regions. The FW-CADIS method has been implemented in the ADVANTG/MCNP framework and has been fully automated within the MAVRIC sequence of SCALE 6. Results of the application of the method to enabling the calculation of dose rates throughout an entire full-scale pressurized-water reactor facility are presented and discussed. (authors)

  15. Measuring the contribution of water and green space amenities to housing values: an application and comparison of spatially weighted hedonic models

    Science.gov (United States)

    Seong-Hoon Cho; J. Michael Bowker; William M. Park

    2006-01-01

    This study estimates the influence of proximity to water bodies and park amenities on residential housing values in Knox County, Tennessee, using the hedonic price approach. Values for proximity to water bodies and parks are first estimated globally with a standard ordinary least squares (OLS) model. A locally weighted regression model is then employed to investigate...

  16. Nutritional status and weight gain in pregnant women.

    Science.gov (United States)

    Sato, Ana Paula Sayuri; Fujimori, Elizabeth

    2012-01-01

    This study described the nutritional status of 228 pregnant women and the influence of this on birth weight. This is a retrospective study, developed in a health center in the municipality of São Paulo, with data obtained from medical records. Linear regression analysis was carried out. An association was verified between the initial and final nutritional status (ppregnancy underweight was higher compared those who started overweight/obese (p=0.005). Weight gain was insufficient for 43.4% of the pregnant women with adequate initial weight and for 36.4% of all the pregnant women studied. However, 37.1% of those who began the pregnancy overweight/obese finished with excessive weight gain, a condition that ultimately affected almost a quarter of the pregnant women. Anemia and low birth weight were uncommon, however, in the linear regression analysis, birth weight was associated with weight gain (pimportance of nutritional care before and during pregnancy to promote maternal-infant health.

  17. A gentle introduction to quantile regression for ecologists

    Science.gov (United States)

    Cade, B.S.; Noon, B.R.

    2003-01-01

    Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a more complete view of possible causal relationships between variables in ecological processes. Typically, all the factors that affect ecological processes are not measured and included in the statistical models used to investigate relationships between variables associated with those processes. As a consequence, there may be a weak or no predictive relationship between the mean of the response variable (y) distribution and the measured predictive factors (X). Yet there may be stronger, useful predictive relationships with other parts of the response variable distribution. This primer relates quantile regression estimates to prediction intervals in parametric error distribution regression models (eg least squares), and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of the estimates for homogeneous and heterogeneous regression models.

  18. Determinants of Inequality in Cameroon: A Regression-Based ...

    African Journals Online (AJOL)

    This paper applies the regression-based inequality decomposition approach to explore determinants of income inequality in Cameroon using the 2007 Cameroon household consumption survey. The contribution of each source to measured income inequality is the sum of its weighted marginal contributions in all possible ...

  19. Local patient dose diagnostic reference levels in pediatric interventional cardiology in Chile using age bands and patient weight values

    Energy Technology Data Exchange (ETDEWEB)

    Ubeda, Carlos, E-mail: cubeda@uta.cl [Medical Technology Department, Radiological Sciences Center, Health Sciences Faculty, Tarapaca University, Arica 1000000 (Chile); Miranda, Patricia [Hemodynamic Department, Cardiovascular Service, Luis Calvo Mackenna Hospital, Santiago 7500539 (Chile); Vano, Eliseo [Radiology Department, Faculty of Medicine, Complutense University and IdIS, San Carlos Hospital, Madrid 28040 (Spain)

    2015-02-15

    Purpose: To present the results of a patient dose evaluation program in pediatric cardiology and propose local diagnostic reference levels (DRLs) for different types of procedure and age range, in addition to suggesting approaches to correlate patient dose values with patient weight. This study was the first conducted in Latin America for pediatric interventional cardiology under the auspices of the International Atomic Energy Agency. Methods: Over three years, the following data regarding demographic and patient dose values were collected: age, gender, weight, height, number of cine series, total number of cine frames, fluoroscopy time (FT), and two dosimetric quantities, dose-area product (DAP) and cumulative dose (CD), at the patient entrance reference point. The third quartile values for FT, DAP, CD, number of cine series, and the DAP/body weight ratio were proposed as the set of quantities to use as local DRLs. Results: Five hundred and seventeen patients were divided into four age groups. Sample sizes by age group were 120 for <1 yr; 213 for 1 to <5 yr; 82 for 5 to <10 yr; and 102 for 10 to <16 yr. The third quartile values obtained for DAP by diagnostic and therapeutic procedures and age range were 1.17 and 1.11 Gy cm{sup 2} for <1 yr; 1.74 and 1.90 Gy cm{sup 2} for 1 to <5 yr; 2.83 and 3.22 Gy cm{sup 2} for 5 to <10 yr; and 7.34 and 8.68 Gy cm{sup 2} for 10 to <16 yr, respectively. The third quartile value obtained for the DAP/body weight ratio for the full sample of procedures was 0.17 (Gy cm{sup 2}/kg) for diagnostic and therapeutic procedures. Conclusions: The data presented in this paper are an initial attempt at establishing local DRLs in pediatric interventional cardiology, from a large sample of procedures for the standard age bands used in Europe, complemented with the values of the ratio between DAP and patient weight. This permits a rough estimate of DRLs for different patient weights and the refining of these values for the age bands when there

  20. Local patient dose diagnostic reference levels in pediatric interventional cardiology in Chile using age bands and patient weight values

    International Nuclear Information System (INIS)

    Ubeda, Carlos; Miranda, Patricia; Vano, Eliseo

    2015-01-01

    Purpose: To present the results of a patient dose evaluation program in pediatric cardiology and propose local diagnostic reference levels (DRLs) for different types of procedure and age range, in addition to suggesting approaches to correlate patient dose values with patient weight. This study was the first conducted in Latin America for pediatric interventional cardiology under the auspices of the International Atomic Energy Agency. Methods: Over three years, the following data regarding demographic and patient dose values were collected: age, gender, weight, height, number of cine series, total number of cine frames, fluoroscopy time (FT), and two dosimetric quantities, dose-area product (DAP) and cumulative dose (CD), at the patient entrance reference point. The third quartile values for FT, DAP, CD, number of cine series, and the DAP/body weight ratio were proposed as the set of quantities to use as local DRLs. Results: Five hundred and seventeen patients were divided into four age groups. Sample sizes by age group were 120 for <1 yr; 213 for 1 to <5 yr; 82 for 5 to <10 yr; and 102 for 10 to <16 yr. The third quartile values obtained for DAP by diagnostic and therapeutic procedures and age range were 1.17 and 1.11 Gy cm 2 for <1 yr; 1.74 and 1.90 Gy cm 2 for 1 to <5 yr; 2.83 and 3.22 Gy cm 2 for 5 to <10 yr; and 7.34 and 8.68 Gy cm 2 for 10 to <16 yr, respectively. The third quartile value obtained for the DAP/body weight ratio for the full sample of procedures was 0.17 (Gy cm 2 /kg) for diagnostic and therapeutic procedures. Conclusions: The data presented in this paper are an initial attempt at establishing local DRLs in pediatric interventional cardiology, from a large sample of procedures for the standard age bands used in Europe, complemented with the values of the ratio between DAP and patient weight. This permits a rough estimate of DRLs for different patient weights and the refining of these values for the age bands when there may be large differences

  1. [Prediction and spatial distribution of recruitment trees of natural secondary forest based on geographically weighted Poisson model].

    Science.gov (United States)

    Zhang, Ling Yu; Liu, Zhao Gang

    2017-12-01

    Based on the data collected from 108 permanent plots of the forest resources survey in Maoershan Experimental Forest Farm during 2004-2016, this study investigated the spatial distribution of recruitment trees in natural secondary forest by global Poisson regression and geographically weighted Poisson regression (GWPR) with four bandwidths of 2.5, 5, 10 and 15 km. The simulation effects of the 5 regressions and the factors influencing the recruitment trees in stands were analyzed, a description was given to the spatial autocorrelation of the regression residuals on global and local levels using Moran's I. The results showed that the spatial distribution of the number of natural secondary forest recruitment was significantly influenced by stands and topographic factors, especially average DBH. The GWPR model with small scale (2.5 km) had high accuracy of model fitting, a large range of model parameter estimates was generated, and the localized spatial distribution effect of the model parameters was obtained. The GWPR model at small scale (2.5 and 5 km) had produced a small range of model residuals, and the stability of the model was improved. The global spatial auto-correlation of the GWPR model residual at the small scale (2.5 km) was the lowe-st, and the local spatial auto-correlation was significantly reduced, in which an ideal spatial distribution pattern of small clusters with different observations was formed. The local model at small scale (2.5 km) was much better than the global model in the simulation effect on the spatial distribution of recruitment tree number.

  2. Globally COnstrained Local Function Approximation via Hierarchical Modelling, a Framework for System Modelling under Partial Information

    DEFF Research Database (Denmark)

    Øjelund, Henrik; Sadegh, Payman

    2000-01-01

    be obtained. This paper presents a new approach for system modelling under partial (global) information (or the so called Gray-box modelling) that seeks to perserve the benefits of the global as well as local methodologies sithin a unified framework. While the proposed technique relies on local approximations......Local function approximations concern fitting low order models to weighted data in neighbourhoods of the points where the approximations are desired. Despite their generality and convenience of use, local models typically suffer, among others, from difficulties arising in physical interpretation...... simultaneously with the (local estimates of) function values. The approach is applied to modelling of a linear time variant dynamic system under prior linear time invariant structure where local regression fails as a result of high dimensionality....

  3. A new locally weighted K-means for cancer-aided microarray data analysis.

    Science.gov (United States)

    Iam-On, Natthakan; Boongoen, Tossapon

    2012-11-01

    Cancer has been identified as the leading cause of death. It is predicted that around 20-26 million people will be diagnosed with cancer by 2020. With this alarming rate, there is an urgent need for a more effective methodology to understand, prevent and cure cancer. Microarray technology provides a useful basis of achieving this goal, with cluster analysis of gene expression data leading to the discrimination of patients, identification of possible tumor subtypes and individualized treatment. Amongst clustering techniques, k-means is normally chosen for its simplicity and efficiency. However, it does not account for the different importance of data attributes. This paper presents a new locally weighted extension of k-means, which has proven more accurate across many published datasets than the original and other extensions found in the literature.

  4. Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

    Science.gov (United States)

    Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

    2014-07-01

    The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

  5. Weight suppression predicts total weight gain and rate of weight gain in outpatients with anorexia nervosa.

    Science.gov (United States)

    Carter, Frances A; Boden, Joseph M; Jordan, Jennifer; McIntosh, Virginia V W; Bulik, Cynthia M; Joyce, Peter R

    2015-11-01

    The present study sought to replicate the finding of Wildes and Marcus, Behav Res Ther, 50, 266-274, 2012 that higher levels of weight suppression at pretreatment predict greater total weight gain, faster rate of weight gain, and bulimic symptoms amongst patients admitted with anorexia nervosa. Participants were 56 women with anorexia nervosa diagnosed by using strict or lenient weight criteria, who were participating in a randomized controlled psychotherapy trial (McIntosh et al., Am J Psychiatry, 162, 741-747, 2005). Thirty-five women completed outpatient treatment and post-treatment assessment. Weight suppression was the discrepancy between highest lifetime weight at adult height and weight at pretreatment assessment. Outcome variables were total weight gain, rate of weight gain, and bulimic symptoms in the month prior to post-treatment assessment [assessed using the Eating Disorders Examination (Fairburn et al., Binge-Eating: Nature, Assessment and Treatment. New York: Guilford, 1993)]. Weight suppression was positively associated with total weight gain and rate of weight gain over treatment. Regression models showed that this association could not be explained by covariates (age at onset of anorexia nervosa and treatment modality). Weight suppression was not significantly associated with bulimic symptoms in the month prior to post-treatment assessment, regardless of whether bulimic symptoms were examined as continuous or dichotomous variables. The present study reinforces the previous finding that weight suppression predicts total weight gain and rate of weight gain amongst patients being treated for anorexia nervosa. Methodological issues may explain the failure of the present study to find that weight suppression predicts bulimic symptoms. Weight suppression at pretreatment for anorexia nervosa should be assessed routinely and may inform treatment planning. © 2015 Wiley Periodicals, Inc.

  6. Demonstration of a Fiber Optic Regression Probe

    Science.gov (United States)

    Korman, Valentin; Polzin, Kurt A.

    2010-01-01

    The capability to provide localized, real-time monitoring of material regression rates in various applications has the potential to provide a new stream of data for development testing of various components and systems, as well as serving as a monitoring tool in flight applications. These applications include, but are not limited to, the regression of a combusting solid fuel surface, the ablation of the throat in a chemical rocket or the heat shield of an aeroshell, and the monitoring of erosion in long-life plasma thrusters. The rate of regression in the first application is very fast, while the second and third are increasingly slower. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor is optical, using two different, co-located fiber-optics to perform the regression measurement. The disparate optical transmission properties of the two fiber-optics makes it possible to measure the regression rate by monitoring the relative light attenuation through the fibers. As the fibers regress along with the parent material in which they are embedded, the relative light intensities through the two fibers changes, providing a measure of the regression rate. The optical nature of the system makes it relatively easy to use in a variety of harsh, high temperature environments, and it is also unaffected by the presence of electric and magnetic fields. In addition, the sensor could be used to perform optical spectroscopy on the light emitted by a process and collected by fibers, giving localized measurements of various properties. The capability to perform an in-situ measurement of material regression rates is useful in addressing a variety of physical issues in various applications. An in-situ measurement allows for real-time data regarding the erosion rates, providing a quick method for

  7. Prediction of hourly PM2.5 using a space-time support vector regression model

    Science.gov (United States)

    Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

    2018-05-01

    Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

  8. Spontaneous regression of metastatic Merkel cell carcinoma.

    LENUS (Irish Health Repository)

    Hassan, S J

    2010-01-01

    Merkel cell carcinoma is a rare aggressive neuroendocrine carcinoma of the skin predominantly affecting elderly Caucasians. It has a high rate of local recurrence and regional lymph node metastases. It is associated with a poor prognosis. Complete spontaneous regression of Merkel cell carcinoma has been reported but is a poorly understood phenomenon. Here we present a case of complete spontaneous regression of metastatic Merkel cell carcinoma demonstrating a markedly different pattern of events from those previously published.

  9. Accounting for measurement error in log regression models with applications to accelerated testing.

    Science.gov (United States)

    Richardson, Robert; Tolley, H Dennis; Evenson, William E; Lunt, Barry M

    2018-01-01

    In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

  10. Accounting for measurement error in log regression models with applications to accelerated testing.

    Directory of Open Access Journals (Sweden)

    Robert Richardson

    Full Text Available In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

  11. A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China.

    Science.gov (United States)

    Yu, Xianyu; Wang, Yi; Niu, Ruiqing; Hu, Youjian

    2016-05-11

    In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%-19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.

  12. Spontaneous regression of retinopathy of prematurity:incidence and predictive factors

    Directory of Open Access Journals (Sweden)

    Rui-Hong Ju

    2013-08-01

    Full Text Available AIM:To evaluate the incidence of spontaneous regression of changes in the retina and vitreous in active stage of retinopathy of prematurity(ROP and identify the possible relative factors during the regression.METHODS: This was a retrospective, hospital-based study. The study consisted of 39 premature infants with mild ROP showed spontaneous regression (Group A and 17 with severe ROP who had been treated before naturally involuting (Group B from August 2008 through May 2011. Data on gender, single or multiple pregnancy, gestational age, birth weight, weight gain from birth to the sixth week of life, use of oxygen in mechanical ventilation, total duration of oxygen inhalation, surfactant given or not, need for and times of blood transfusion, 1,5,10-min Apgar score, presence of bacterial or fungal or combined infection, hyaline membrane disease (HMD, patent ductus arteriosus (PDA, duration of stay in the neonatal intensive care unit (NICU and duration of ROP were recorded.RESULTS: The incidence of spontaneous regression of ROP with stage 1 was 86.7%, and with stage 2, stage 3 was 57.1%, 5.9%, respectively. With changes in zone Ⅲ regression was detected 100%, in zoneⅡ 46.2% and in zoneⅠ 0%. The mean duration of ROP in spontaneous regression group was 5.65±3.14 weeks, lower than that of the treated ROP group (7.34±4.33 weeks, but this difference was not statistically significant (P=0.201. GA, 1min Apgar score, 5min Apgar score, duration of NICU stay, postnatal age of initial screening and oxygen therapy longer than 10 days were significant predictive factors for the spontaneous regression of ROP (P<0.05. Retinal hemorrhage was the only independent predictive factor the spontaneous regression of ROP (OR 0.030, 95%CI 0.001-0.775, P=0.035.CONCLUSION:This study showed most stage 1 and 2 ROP and changes in zone Ⅲ can spontaneously regression in the end. Retinal hemorrhage is weakly inversely associated with the spontaneous regression.

  13. Research and analyze of physical health using multiple regression analysis

    Directory of Open Access Journals (Sweden)

    T. S. Kyi

    2014-01-01

    Full Text Available This paper represents the research which is trying to create a mathematical model of the "healthy people" using the method of regression analysis. The factors are the physical parameters of the person (such as heart rate, lung capacity, blood pressure, breath holding, weight height coefficient, flexibility of the spine, muscles of the shoulder belt, abdominal muscles, squatting, etc.., and the response variable is an indicator of physical working capacity. After performing multiple regression analysis, obtained useful multiple regression models that can predict the physical performance of boys the aged of fourteen to seventeen years. This paper represents the development of regression model for the sixteen year old boys and analyzed results.

  14. Using Electromyography to Detect the Weightings of the Local Muscle Factors to the Increase of Perceived Exertion During Stepping Exercise

    Directory of Open Access Journals (Sweden)

    Miao-Ju Hsu

    2008-06-01

    Full Text Available Rate of perceived exertion (RPE is a clinically convenient indicator for monitoring exercise intensity in cardiopulmonary rehabilitation. It might not be sensitive enough for clinicians to determine the patients’ physiological status because its association with the cardiovascular system and local muscle factors is unknown. This study used the electromyographic sensor to detect the local muscle fatigue and stabilization of patella, and analyzed the relationship between various local muscle and cardiovascular factors and the increase of RPE during stepping exercise, a common exercise program provided in cardiopulmonary rehabilitation. Ten healthy adults (4 males and 6 females participated in this study. Each subject used their right bare foot to step up onto a 23-cm-high step at a constant speed until the RPE score reached 20. The RPE, heart rate (HR, and surface EMG of the rectus femoris (RF, vastus medialis, and vastus lateralis were recorded at 1-minute intervals during the stepping exercise. The generalized estimating equations (GEE analysis indicated that the increase in RPE significantly correlated with the increase in HR, and decrease in median frequency (MF of the EMG power spectrum of the RF. Experimental results suggest that the increase in RPE during stepping exercise was influenced by the cardiovascular status, localized muscle fatigue in the lower extremities. The weighting of the local muscle factors was more than half of the weighting of the cardiovascular factor.

  15. Embeddings relations between weighted complementary Local Morrey-type spaces and weighted local Morrey-type spaces

    Czech Academy of Sciences Publication Activity Database

    Gogatishvili, Amiran; Mustafayev, R.Ch.; Ünver, T.

    2017-01-01

    Roč. 8, č. 1 (2017), s. 34-49 ISSN 2077-9879 R&D Projects: GA ČR GA13-14743S Institutional support: RVO:67985840 Keywords : local Morrey-type spaces * embeddings * iterated Hardy inequalities Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics http://www.mathnet.ru/ php /archive.phtml?wshow=paper&jrnid=emj&paperid=246&option_lang=rus

  16. Embeddings relations between weighted complementary Local Morrey-type spaces and weighted local Morrey-type spaces

    Czech Academy of Sciences Publication Activity Database

    Gogatishvili, Amiran; Mustafayev, R.Ch.; Ünver, T.

    2017-01-01

    Roč. 8, č. 1 (2017), s. 34-49 ISSN 2077-9879 R&D Projects: GA ČR GA13-14743S Institutional support: RVO:67985840 Keywords : local Morrey-type spaces * embeddings * iterated Hardy inequalities Subject RIV: BA - General Mathematics OBOR OECD: Pure mathematics http://www.mathnet.ru/php/archive.phtml?wshow=paper&jrnid=emj&paperid=246&option_lang=rus

  17. Functional data analysis of generalized regression quantiles

    KAUST Repository

    Guo, Mengmeng; Zhou, Lan; Huang, Jianhua Z.; Hä rdle, Wolfgang Karl

    2013-01-01

    Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

  18. Functional data analysis of generalized regression quantiles

    KAUST Repository

    Guo, Mengmeng

    2013-11-05

    Generalized regression quantiles, including the conditional quantiles and expectiles as special cases, are useful alternatives to the conditional means for characterizing a conditional distribution, especially when the interest lies in the tails. We develop a functional data analysis approach to jointly estimate a family of generalized regression quantiles. Our approach assumes that the generalized regression quantiles share some common features that can be summarized by a small number of principal component functions. The principal component functions are modeled as splines and are estimated by minimizing a penalized asymmetric loss measure. An iterative least asymmetrically weighted squares algorithm is developed for computation. While separate estimation of individual generalized regression quantiles usually suffers from large variability due to lack of sufficient data, by borrowing strength across data sets, our joint estimation approach significantly improves the estimation efficiency, which is demonstrated in a simulation study. The proposed method is applied to data from 159 weather stations in China to obtain the generalized quantile curves of the volatility of the temperature at these stations. © 2013 Springer Science+Business Media New York.

  19. Spatial weighting of Doppler reactivity feedback

    International Nuclear Information System (INIS)

    Carew, J.F.; Diamond, D.J.; Todosow, M.

    1977-12-01

    The spatial weighting of the local Doppler feedback implicit in the determination of the core Doppler feedback reactivity has been investigated. Using a detailed planar PDQ7-II PWR model with local fuel-temperature feedback, the core Doppler spatial weight factor, S, has been determined for various control patterns and power levels. Assuming power-squared weighting of the local Doppler feedback, a simple analytic expression for S has been derived and, based on comparison with the PDQ7-II results, provides a convenient and accurate representation of the Doppler spatial weight factor. The sensitivity of these results to variations in the fuel rod heat transfer coefficients, fuel loading and the magnitude of the Doppler coefficient has also been evaluated. The dependence of the local Doppler coefficient on moderator temperature, boron concentration and control rod density has been determined and found to be weak. Selected comparisons with vendor analyses have been made and indicate general agreement

  20. When homogeneity meets heterogeneity: the geographically weighted regression with spatial lag approach to prenatal care utilization

    Science.gov (United States)

    Shoff, Carla; Chen, Vivian Yi-Ju; Yang, Tse-Chuan

    2014-01-01

    Using geographically weighted regression (GWR), a recent study by Shoff and colleagues (2012) investigated the place-specific risk factors for prenatal care utilization in the US and found that most of the relationships between late or not prenatal care and its determinants are spatially heterogeneous. However, the GWR approach may be subject to the confounding effect of spatial homogeneity. The goal of this study is to address this concern by including both spatial homogeneity and heterogeneity into the analysis. Specifically, we employ an analytic framework where a spatially lagged (SL) effect of the dependent variable is incorporated into the GWR model, which is called GWR-SL. Using this innovative framework, we found evidence to argue that spatial homogeneity is neglected in the study by Shoff et al. (2012) and the results are changed after considering the spatially lagged effect of prenatal care utilization. The GWR-SL approach allows us to gain a place-specific understanding of prenatal care utilization in US counties. In addition, we compared the GWR-SL results with the results of conventional approaches (i.e., OLS and spatial lag models) and found that GWR-SL is the preferred modeling approach. The new findings help us to better estimate how the predictors are associated with prenatal care utilization across space, and determine whether and how the level of prenatal care utilization in neighboring counties matters. PMID:24893033

  1. Weighted Regressions on Time, Discharge, and Season (WRTDS), with an application to Chesapeake Bay River inputs

    Science.gov (United States)

    Hirsch, Robert M.; Moyer, Douglas; Archfield, Stacey A.

    2010-01-01

    A new approach to the analysis of long-term surface water-quality data is proposed and implemented. The goal of this approach is to increase the amount of information that is extracted from the types of rich water-quality datasets that now exist. The method is formulated to allow for maximum flexibility in representations of the long-term trend, seasonal components, and discharge-related components of the behavior of the water-quality variable of interest. It is designed to provide internally consistent estimates of the actual history of concentrations and fluxes as well as histories that eliminate the influence of year-to-year variations in streamflow. The method employs the use of weighted regressions of concentrations on time, discharge, and season. Finally, the method is designed to be useful as a diagnostic tool regarding the kinds of changes that are taking place in the watershed related to point sources, groundwater sources, and surface-water nonpoint sources. The method is applied to datasets for the nine large tributaries of Chesapeake Bay from 1978 to 2008. The results show a wide range of patterns of change in total phosphorus and in dissolved nitrate plus nitrite. These results should prove useful in further examination of the causes of changes, or lack of changes, and may help inform decisions about future actions to reduce nutrient enrichment in the Chesapeake Bay and its watershed.

  2. Adjusting for Confounding in Early Postlaunch Settings: Going Beyond Logistic Regression Models.

    Science.gov (United States)

    Schmidt, Amand F; Klungel, Olaf H; Groenwold, Rolf H H

    2016-01-01

    Postlaunch data on medical treatments can be analyzed to explore adverse events or relative effectiveness in real-life settings. These analyses are often complicated by the number of potential confounders and the possibility of model misspecification. We conducted a simulation study to compare the performance of logistic regression, propensity score, disease risk score, and stabilized inverse probability weighting methods to adjust for confounding. Model misspecification was induced in the independent derivation dataset. We evaluated performance using relative bias confidence interval coverage of the true effect, among other metrics. At low events per coefficient (1.0 and 0.5), the logistic regression estimates had a large relative bias (greater than -100%). Bias of the disease risk score estimates was at most 13.48% and 18.83%. For the propensity score model, this was 8.74% and >100%, respectively. At events per coefficient of 1.0 and 0.5, inverse probability weighting frequently failed or reduced to a crude regression, resulting in biases of -8.49% and 24.55%. Coverage of logistic regression estimates became less than the nominal level at events per coefficient ≤5. For the disease risk score, inverse probability weighting, and propensity score, coverage became less than nominal at events per coefficient ≤2.5, ≤1.0, and ≤1.0, respectively. Bias of misspecified disease risk score models was 16.55%. In settings with low events/exposed subjects per coefficient, disease risk score methods can be useful alternatives to logistic regression models, especially when propensity score models cannot be used. Despite better performance of disease risk score methods than logistic regression and propensity score models in small events per coefficient settings, bias, and coverage still deviated from nominal.

  3. LARF: Instrumental Variable Estimation of Causal Effects through Local Average Response Functions

    Directory of Open Access Journals (Sweden)

    Weihua An

    2016-07-01

    Full Text Available LARF is an R package that provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument (i.e., the treatment inducement are binary. The method (Abadie 2003 involves two steps. First, pseudo-weights are constructed from the probability of receiving the treatment inducement. By default LARF estimates the probability by a probit regression. It also provides semiparametric power series estimation of the probability and allows users to employ other external methods to estimate the probability. Second, the pseudo-weights are used to estimate the local average response function conditional on treatment and covariates. LARF provides both least squares and maximum likelihood estimates of the conditional treatment effects.

  4. First-day newborn weight loss predicts in-hospital weight nadir for breastfeeding infants.

    Science.gov (United States)

    Flaherman, Valerie J; Bokser, Seth; Newman, Thomas B

    2010-08-01

    Exclusive breastfeeding reduces infant infectious disease. Losing > or =10% birth weight may lead to formula use. The predictive value of first-day weight loss for subsequent weight loss has not been studied. The objective of the present study was to evaluate the relationship between weight loss at or =10%. For 1,049 infants, we extracted gestational age, gender, delivery method, feeding type, and weights from medical records. Weight nadir was defined as the lowest weight recorded during birth hospitalization. We used multivariate logistic regression to assess the effect of first-day weight loss on subsequent in-hospital weight loss. Mean in-hospital weight nadir was 6.0 +/- 2.6%, and mean age at in-hospital weight nadir was 38.7 +/- 18.5 hours. While in the hospital 6.4% of infants lost > or =10% of birth weight. Infants losing > or =4.5% birth weight at or =10% (adjusted odds ratio 3.57 [1.75, 7.28]). In this cohort, 798 (76.1%) infants did not have documented weight gain while in the hospital. Early weight loss predicts higher risk of > or =10% in-hospital weight loss. Infants with high first-day weight loss could be targeted for further research into improved interventions to promote breastfeeding.

  5. Differentiating regressed melanoma from regressed lichenoid keratosis.

    Science.gov (United States)

    Chan, Aegean H; Shulman, Kenneth J; Lee, Bonnie A

    2017-04-01

    Distinguishing regressed lichen planus-like keratosis (LPLK) from regressed melanoma can be difficult on histopathologic examination, potentially resulting in mismanagement of patients. We aimed to identify histopathologic features by which regressed melanoma can be differentiated from regressed LPLK. Twenty actively inflamed LPLK, 12 LPLK with regression and 15 melanomas with regression were compared and evaluated by hematoxylin and eosin staining as well as Melan-A, microphthalmia transcription factor (MiTF) and cytokeratin (AE1/AE3) immunostaining. (1) A total of 40% of regressed melanomas showed complete or near complete loss of melanocytes within the epidermis with Melan-A and MiTF immunostaining, while 8% of regressed LPLK exhibited this finding. (2) Necrotic keratinocytes were seen in the epidermis in 33% regressed melanomas as opposed to all of the regressed LPLK. (3) A dense infiltrate of melanophages in the papillary dermis was seen in 40% of regressed melanomas, a feature not seen in regressed LPLK. In summary, our findings suggest that a complete or near complete loss of melanocytes within the epidermis strongly favors a regressed melanoma over a regressed LPLK. In addition, necrotic epidermal keratinocytes and the presence of a dense band-like distribution of dermal melanophages can be helpful in differentiating these lesions. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  6. Maternal Pre-pregnancy BMI, Gestational Weight Gain, and Infant Birth Weight: A Within-Family Analysis in the United States

    OpenAIRE

    Ji Yan

    2014-01-01

    In the United States, the high prevalence of unhealthy preconception body weight and inappropriate gestational weight gain among pregnant women is an important public health concern. However, the relationship among pre-pregnancy BMI, gestational weight gain, and newborn birth weight has not been well established. This study uses a very large dataset of sibling births and a within-family design to thoroughly address this issue. The baseline regression controlling for mother fixed effects indic...

  7. Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai

    Science.gov (United States)

    Shafizadeh-Moghadam, Hossein; Helbich, Marco

    2015-03-01

    The rapid growth of megacities requires special attention among urban planners worldwide, and particularly in Mumbai, India, where growth is very pronounced. To cope with the planning challenges this will bring, developing a retrospective understanding of urban land-use dynamics and the underlying driving-forces behind urban growth is a key prerequisite. This research uses regression-based land-use change models - and in particular non-spatial logistic regression models (LR) and auto-logistic regression models (ALR) - for the Mumbai region over the period 1973-2010, in order to determine the drivers behind spatiotemporal urban expansion. Both global models are complemented by a local, spatial model, the so-called geographically weighted logistic regression (GWLR) model, one that explicitly permits variations in driving-forces across space. The study comes to two main conclusions. First, both global models suggest similar driving-forces behind urban growth over time, revealing that LRs and ALRs result in estimated coefficients with comparable magnitudes. Second, all the local coefficients show distinctive temporal and spatial variations. It is therefore concluded that GWLR aids our understanding of urban growth processes, and so can assist context-related planning and policymaking activities when seeking to secure a sustainable urban future.

  8. Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis

    Directory of Open Access Journals (Sweden)

    Stefanos Georganos

    2018-02-01

    Full Text Available In object-based image analysis (OBIA, the appropriate parametrization of segmentation algorithms is crucial for obtaining satisfactory image classification results. One of the ways this can be done is by unsupervised segmentation parameter optimization (USPO. A popular USPO method does this through the optimization of a “global score” (GS, which minimizes intrasegment heterogeneity and maximizes intersegment heterogeneity. However, the calculated GS values are sensitive to the minimum and maximum ranges of the candidate segmentations. Previous research proposed the use of fixed minimum/maximum threshold values for the intrasegment/intersegment heterogeneity measures to deal with the sensitivity of user-defined ranges, but the performance of this approach has not been investigated in detail. In the context of a remote sensing very-high-resolution urban application, we show the limitations of the fixed threshold approach, both in a theoretical and applied manner, and instead propose a novel solution to identify the range of candidate segmentations using local regression trend analysis. We found that the proposed approach showed significant improvements over the use of fixed minimum/maximum values, is less subjective than user-defined threshold values and, thus, can be of merit for a fully automated procedure and big data applications.

  9. Applications of Monte Carlo method to nonlinear regression of rheological data

    Science.gov (United States)

    Kim, Sangmo; Lee, Junghaeng; Kim, Sihyun; Cho, Kwang Soo

    2018-02-01

    In rheological study, it is often to determine the parameters of rheological models from experimental data. Since both rheological data and values of the parameters vary in logarithmic scale and the number of the parameters is quite large, conventional method of nonlinear regression such as Levenberg-Marquardt (LM) method is usually ineffective. The gradient-based method such as LM is apt to be caught in local minima which give unphysical values of the parameters whenever the initial guess of the parameters is far from the global optimum. Although this problem could be solved by simulated annealing (SA), the Monte Carlo (MC) method needs adjustable parameter which could be determined in ad hoc manner. We suggest a simplified version of SA, a kind of MC methods which results in effective values of the parameters of most complicated rheological models such as the Carreau-Yasuda model of steady shear viscosity, discrete relaxation spectrum and zero-shear viscosity as a function of concentration and molecular weight.

  10. Locally Available Dietary Menus Promote Weight Gain among Acutely Malnourished Children Undergoing a Community-Based Nutrition Rehabilitation Program in Uganda

    International Nuclear Information System (INIS)

    Mugisha, Jennifer; Kakande, Celia

    2014-01-01

    Full text: Background: A significant proportion of Uganda’s children still suffer from acute malnutrition, despite decades of government, donor and agency investment in basic health services. The demand for the WHO recommended Ready to Use Therapeutic Foods (RUTF) for community based nutrition rehabilitation programs (CMAM) and costs involved have been overwhelming, thus prompting the need for alternative, sustainable, local solutions. Methods The Management Sciences for Health STRIDES for Family Health Project (MSH-STRIDES), funded by USAID, implemented a community-based nutritional rehabilitation intervention using the principles of Positive Deviance. The approach identified solutions (practices) already being used by community members with well-nourished children despite not having access to special resources (positive deviants). Community volunteers encouraged children to be assessed for nutrition during special growth monitoring sessions. Children found malnourished were enrolled into a nutrition rehabilitation program also known as a hearth cycle, in a volunteer’s home. Project staff and trained volunteers followed-up with malnourished children in their homes and invited the caregivers to bring them to participate in hearth cycles over 26 days. Caregivers were taught to recognize malnutrition and to treat it with supervised supplemental feedings of menu-mixtures of locally prepared, nutrient-dense foods. Weight gain was used as an outcome measure. Children were linked to health centers within the locality for curative services. MSH-STRIDES provided training to staff in the facilities and equipped them to serve as referral points for the children identified from the community. Results: Hearth cycles were conducted in 230 villages from 34 sub-counties in 11 out of 15 project districts. A total of 1336 health workers and 283 caregivers were trained and involved in the implementation of the community model. Overall, 2525 children with moderate and severe

  11. Assessing the influence of traffic-related air pollution on risk of term low birth weight on the basis of land-use-based regression models and measures of air toxics.

    Science.gov (United States)

    Ghosh, Jo Kay C; Wilhelm, Michelle; Su, Jason; Goldberg, Daniel; Cockburn, Myles; Jerrett, Michael; Ritz, Beate

    2012-06-15

    Few studies have examined associations of birth outcomes with toxic air pollutants (air toxics) in traffic exhaust. This study included 8,181 term low birth weight (LBW) children and 370,922 term normal-weight children born between January 1, 1995, and December 31, 2006, to women residing within 5 miles (8 km) of an air toxics monitoring station in Los Angeles County, California. Additionally, land-use-based regression (LUR)-modeled estimates of levels of nitric oxide, nitrogen dioxide, and nitrogen oxides were used to assess the influence of small-area variations in traffic pollution. The authors examined associations with term LBW (≥37 weeks' completed gestation and birth weight variations) resulted in 2%-5% increased odds per interquartile-range increase in third-trimester benzene, toluene, ethyl benzene, and xylene exposures, with some confidence intervals containing the null value. This analysis highlights the importance of both spatial and temporal contributions to air pollution in epidemiologic birth outcome studies.

  12. Weight-based discrimination: an ubiquitary phenomenon?

    Science.gov (United States)

    Sikorski, C; Spahlholz, J; Hartlev, M; Riedel-Heller, S G

    2016-02-01

    Despite strong indications of a high prevalence of weight-related stigmatization in individuals with obesity, limited attention has been given to the role of weight discrimination in examining the stigma obesity. Studies, up to date, rely on a limited basis of data sets and additional studies are needed to confirm the findings of previous studies. In particular, data for Europe are lacking, and are needed in light of a recent ruling of the European Court of Justice that addressed weight-based discrimination. The data were derived from a large representative telephone survey in Germany (n=3003). The dependent variable, weight-based discrimination, was assessed with a one-item question. The lifetime prevalence of weight discrimination across different sociodemographic variables was determined. Logistic regression models were used to assess the association of independent and dependent variables. A sub-group analysis was conducted analyzing all participants with a body mass index ⩾25 kg m(-)(2). The overall prevalence of weight-based discrimination was 7.3%. Large differences, however, were observed regarding weight status. In normal weight and overweight participants the prevalence was 5.6%, but this number doubled in participants with obesity class I (10.2%), and quadrupled in participants with obesity class II (18.7%) and underweight (19.7%). In participants with obesity class III, every third participant reported accounts of weight-based discrimination (38%). In regression models, after adjustment, the associations of weight status and female gender (odds ratio: 2.59, PDiscrimination seems to be an ubiquitary phenomenon at least for some groups that are at special risk, such as heavier individuals and women. Our findings therefore emphasize the need for research and intervention on weight discrimination among adults with obesity, including anti-discrimination legislation.

  13. Multivariate and semiparametric kernel regression

    OpenAIRE

    Härdle, Wolfgang; Müller, Marlene

    1997-01-01

    The paper gives an introduction to theory and application of multivariate and semiparametric kernel smoothing. Multivariate nonparametric density estimation is an often used pilot tool for examining the structure of data. Regression smoothing helps in investigating the association between covariates and responses. We concentrate on kernel smoothing using local polynomial fitting which includes the Nadaraya-Watson estimator. Some theory on the asymptotic behavior and bandwidth selection is pro...

  14. Direct modeling of regression effects for transition probabilities in the progressive illness-death model

    DEFF Research Database (Denmark)

    Azarang, Leyla; Scheike, Thomas; de Uña-Álvarez, Jacobo

    2017-01-01

    In this work, we present direct regression analysis for the transition probabilities in the possibly non-Markov progressive illness–death model. The method is based on binomial regression, where the response is the indicator of the occupancy for the given state along time. Randomly weighted score...

  15. Permitted water pollution discharges and population cancer and non-cancer mortality: toxicity weights and upstream discharge effects in US rural-urban areas.

    Science.gov (United States)

    Hendryx, Michael; Conley, Jamison; Fedorko, Evan; Luo, Juhua; Armistead, Matthew

    2012-04-02

    The study conducts statistical and spatial analyses to investigate amounts and types of permitted surface water pollution discharges in relation to population mortality rates for cancer and non-cancer causes nationwide and by urban-rural setting. Data from the Environmental Protection Agency's (EPA) Discharge Monitoring Report (DMR) were used to measure the location, type, and quantity of a selected set of 38 discharge chemicals for 10,395 facilities across the contiguous US. Exposures were refined by weighting amounts of chemical discharges by their estimated toxicity to human health, and by estimating the discharges that occur not only in a local county, but area-weighted discharges occurring upstream in the same watershed. Centers for Disease Control and Prevention (CDC) mortality files were used to measure age-adjusted population mortality rates for cancer, kidney disease, and total non-cancer causes. Analysis included multiple linear regressions to adjust for population health risk covariates. Spatial analyses were conducted by applying geographically weighted regression to examine the geographic relationships between releases and mortality. Greater non-carcinogenic chemical discharge quantities were associated with significantly higher non-cancer mortality rates, regardless of toxicity weighting or upstream discharge weighting. Cancer mortality was higher in association with carcinogenic discharges only after applying toxicity weights. Kidney disease mortality was related to higher non-carcinogenic discharges only when both applying toxicity weights and including upstream discharges. Effects for kidney mortality and total non-cancer mortality were stronger in rural areas than urban areas. Spatial results show correlations between non-carcinogenic discharges and cancer mortality for much of the contiguous United States, suggesting that chemicals not currently recognized as carcinogens may contribute to cancer mortality risk. The geographically weighted

  16. Permitted water pollution discharges and population cancer and non-cancer mortality: toxicity weights and upstream discharge effects in US rural-urban areas

    Directory of Open Access Journals (Sweden)

    Hendryx Michael

    2012-04-01

    Full Text Available Abstract Background The study conducts statistical and spatial analyses to investigate amounts and types of permitted surface water pollution discharges in relation to population mortality rates for cancer and non-cancer causes nationwide and by urban-rural setting. Data from the Environmental Protection Agency's (EPA Discharge Monitoring Report (DMR were used to measure the location, type, and quantity of a selected set of 38 discharge chemicals for 10,395 facilities across the contiguous US. Exposures were refined by weighting amounts of chemical discharges by their estimated toxicity to human health, and by estimating the discharges that occur not only in a local county, but area-weighted discharges occurring upstream in the same watershed. Centers for Disease Control and Prevention (CDC mortality files were used to measure age-adjusted population mortality rates for cancer, kidney disease, and total non-cancer causes. Analysis included multiple linear regressions to adjust for population health risk covariates. Spatial analyses were conducted by applying geographically weighted regression to examine the geographic relationships between releases and mortality. Results Greater non-carcinogenic chemical discharge quantities were associated with significantly higher non-cancer mortality rates, regardless of toxicity weighting or upstream discharge weighting. Cancer mortality was higher in association with carcinogenic discharges only after applying toxicity weights. Kidney disease mortality was related to higher non-carcinogenic discharges only when both applying toxicity weights and including upstream discharges. Effects for kidney mortality and total non-cancer mortality were stronger in rural areas than urban areas. Spatial results show correlations between non-carcinogenic discharges and cancer mortality for much of the contiguous United States, suggesting that chemicals not currently recognized as carcinogens may contribute to cancer

  17. A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China

    Directory of Open Access Journals (Sweden)

    Xianyu Yu

    2016-05-01

    Full Text Available In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%–19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.

  18. ESTIMATION OF YELLOWFIN TUNA PRODUCTION LANDED IN BENOA PORT WITH WEIGHT-WEIGHT, LENGTH-WEIGHT RELATIONSHIPS AND CONDITION FACTOR APPROACHES

    Directory of Open Access Journals (Sweden)

    Irwan Jatmiko

    2017-01-01

    Full Text Available Yellowfin tuna (Thunnus albacares is one of the important catch for the fishing industry in Indonesia. Length-weight relationship study is one of important tools to support fisheries management. However it could not be done to yellowfin tuna landed in Benoa port since they are in the form of gilled-gutted condition. The objectives of this study are to determine the relationship between gilled-gutted weight (GW and whole weight (WW, to calculate length weight relationship between fork length (FL and estimated whole weight (WW and to assess the relative condition factor (Kn of yellowfin tuna in Eastern Indian Ocean. Data were collected from three landing sites i.e. Malang, East Java; Benoa, Bali and Kupang, East Nusa Tenggara from January 2013 to February 2014. Linear regression analysis applied to test the significance baseline between weight-weight relationships and log transformed length weight relationship. Relative condition factor (Kn used to identify fish condition among length groups and months. The results showed a significant positive linear relationships between whole weight (WW and gilled-gutted weight (GW of T. albacares (p<0.001. There was a significant positive linier relationships between log transformed fork length and log transformed whole weight of T. albacares (p<0.001. Relative condition factor (Kn showed declining pattern along with length increase and varied among months. The findings from this study provide data for management of yellowfin tuna stock and population.

  19. Interpreting Multiple Linear Regression: A Guidebook of Variable Importance

    Science.gov (United States)

    Nathans, Laura L.; Oswald, Frederick L.; Nimon, Kim

    2012-01-01

    Multiple regression (MR) analyses are commonly employed in social science fields. It is also common for interpretation of results to typically reflect overreliance on beta weights, often resulting in very limited interpretations of variable importance. It appears that few researchers employ other methods to obtain a fuller understanding of what…

  20. Random regression analysis for body weights and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus).

    Science.gov (United States)

    He, Jie; Zhao, Yunfeng; Zhao, Jingli; Gao, Jin; Xu, Pao; Yang, Runqing

    2018-02-01

    To genetically analyse growth traits in genetically improved farmed tilapia (GIFT), the body weight (BWE) and main morphological traits, including body length (BL), body depth (BD), body width (BWI), head length (HL) and length of the caudal peduncle (CPL), were measured six times in growth duration on 1451 fish from 45 mixed families of full and half sibs. A random regression model (RRM) was used to model genetic changes of the growth traits with days of age and estimate the heritability for any growth point and genetic correlations between pairwise growth points. Using the covariance function based on optimal RRMs, the heritabilities were estimated to be from 0.102 to 0.662 for BWE, 0.157 to 0.591 for BL, 0.047 to 0.621 for BD, 0.018 to 0.577 for BWI, 0.075 to 0.597 for HL and 0.032 to 0.610 for CPL between 60 and 140 days of age. All genetic correlations exceeded 0.5 between pairwise growth points. Moreover, the traits at initial days of age showed less correlation with those at later days of age. With phenotypes observed repeatedly, the model choice showed that the optimal RRMs could more precisely predict breeding values at a specific growth time than repeatability models or multiple trait animal models, which enhanced the efficiency of selection for the BWE and main morphological traits.

  1. Deriving Genomic Breeding Values for Residual Feed Intake from Covariance Functions of Random Regression Models

    DEFF Research Database (Denmark)

    Strathe, Anders B; Mark, Thomas; Nielsen, Bjarne

    2014-01-01

    Random regression models were used to estimate covariance functions between cumulated feed intake (CFI) and body weight (BW) in 8424 Danish Duroc pigs. Random regressions on second order Legendre polynomials of age were used to describe genetic and permanent environmental curves in BW and CFI...

  2. Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index.

    Directory of Open Access Journals (Sweden)

    Ping Wang

    2011-02-01

    Full Text Available Weight regain after weight loss is common. In the Diogenes dietary intervention study, high protein and low glycemic index (GI diet improved weight maintenance.To identify blood predictors for weight change after weight loss following the dietary intervention within the Diogenes study.Blood samples were collected at baseline and after 8-week low caloric diet-induced weight loss from 48 women who continued to lose weight and 48 women who regained weight during subsequent 6-month dietary intervention period with 4 diets varying in protein and GI levels. Thirty-one proteins and 3 steroid hormones were measured.Angiotensin I converting enzyme (ACE was the most important predictor. Its greater reduction during the 8-week weight loss was related to continued weight loss during the subsequent 6 months, identified by both Logistic Regression and Random Forests analyses. The prediction power of ACE was influenced by immunoproteins, particularly fibrinogen. Leptin, luteinizing hormone and some immunoproteins showed interactions with dietary protein level, while interleukin 8 showed interaction with GI level on the prediction of weight maintenance. A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%.A selected panel of blood proteins/steroids can predict the weight change after weight loss. ACE may play an important role in weight maintenance. The interactions of blood factors with dietary components are important for personalized dietary advice after weight loss.ClinicalTrials.gov NCT00390637.

  3. Local Model Checking of Weighted CTL with Upper-Bound Constraints

    DEFF Research Database (Denmark)

    Jensen, Jonas Finnemann; Larsen, Kim Guldstrand; Srba, Jiri

    2013-01-01

    We present a symbolic extension of dependency graphs by Liu and Smolka in order to model-check weighted Kripke structures against the logic CTL with upper-bound weight constraints. Our extension introduces a new type of edges into dependency graphs and lifts the computation of fixed-points from...

  4. A Mixed Application of Geographically Weighted Regression and Unsupervised Classification for Analyzing Latex Yield Variability in Yunnan, China

    Directory of Open Access Journals (Sweden)

    Oh Seok Kim

    2017-05-01

    Full Text Available This paper introduces a mixed method approach for analyzing the determinants of natural latex yields and the associated spatial variations and identifying the most suitable regions for producing latex. Geographically Weighted Regressions (GWR and Iterative Self-Organizing Data Analysis Technique (ISODATA are jointly applied to the georeferenced data points collected from the rubber plantations in Xishuangbanna (in Yunnan province, south China and other remotely-sensed spatial data. According to the GWR models, Age of rubber tree, Percent of clay in soil, Elevation, Solar radiation, Population, Distance from road, Distance from stream, Precipitation, and Mean temperature turn out statistically significant, indicating that these are the major determinants shaping latex yields at the prefecture level. However, the signs and magnitudes of the parameter estimates at the aggregate level are different from those at the lower spatial level, and the differences are due to diverse reasons. The ISODATA classifies the landscape into three categories: high, medium, and low potential yields. The map reveals that Mengla County has the majority of land with high potential yield, while Jinghong City and Menghai County show lower potential yield. In short, the mixed method can offer a means of providing greater insights in the prediction of agricultural production.

  5. SU-E-P-08: Establishment of Local Diagnostic Reference Levels of Routine Abdomen Exam in Computed Tomography According to Body Weight

    International Nuclear Information System (INIS)

    Wang, H; Wang, Y; Weng, H

    2015-01-01

    Purpose: The national diagnostic reference levels (NDRLs) is an efficient, concise and powerful standard for optimizing radiation protection of a patient. However, for each hospital the dose-reducing potential of focusing on establishment of local DRLs (LDRLs). A lot of study reported that Computed tomography exam contributed majority radiation dose in different medical modalities, therefore, routine abdomen CT exam was choose in initial pilot study in our study. Besides the mAs of routine abdomen CT exam was decided automatic exposure control by linear attenuation is relate to body shape of patient. In this study we would like to establish the local diagnostic reference levels of routine abdomen exam in computed tomography according to body weight of patient. Methods and Materials: There are two clinical CT scanners (a Toshiba Aquilion and a Siemens Sensation) were performed in this study. For CT examinations the basic recommended dosimetric quantity is the Computed Tomography Dose Index (CTDI). The patient sample involved 82 adult patients of both sexes and divided into three groups by their body weight (50–60 kg, 60–70 kg, 70–80 kg).Carried out the routine abdomen examinations, and all exposure parameters have been collected and the corresponding CTDIv and DLP values have been determined. The average values were compared with the European DRLs. Results: The majority of patients (75%) were between 50–70 Kg of body weight, the numbers of patient in each group of weight were 40–50:7; 50–60:29; 60–70:33; 70–80:13. The LDRLs in each group were 10.81mGy, 14.46mGy, 20.27mGy and 21.04mGy, respectively. The DLP were 477mGy, 630mGy, 887mGy and 959mGy, respectively. No matter which group the LDRLs were lower than European DRLs. Conclusions: We would like to state that this was a pioneer work in local hospital in Chiayi. We hope that this may lead the way to further developments in Taiwan

  6. SU-E-P-08: Establishment of Local Diagnostic Reference Levels of Routine Abdomen Exam in Computed Tomography According to Body Weight

    Energy Technology Data Exchange (ETDEWEB)

    Wang, H; Wang, Y; Weng, H [Chiayi Chang Gung Memorial Hospital of The C.G.M.F, Puzi City, Chiayi County, Taiwan (China)

    2015-06-15

    Purpose: The national diagnostic reference levels (NDRLs) is an efficient, concise and powerful standard for optimizing radiation protection of a patient. However, for each hospital the dose-reducing potential of focusing on establishment of local DRLs (LDRLs). A lot of study reported that Computed tomography exam contributed majority radiation dose in different medical modalities, therefore, routine abdomen CT exam was choose in initial pilot study in our study. Besides the mAs of routine abdomen CT exam was decided automatic exposure control by linear attenuation is relate to body shape of patient. In this study we would like to establish the local diagnostic reference levels of routine abdomen exam in computed tomography according to body weight of patient. Methods and Materials: There are two clinical CT scanners (a Toshiba Aquilion and a Siemens Sensation) were performed in this study. For CT examinations the basic recommended dosimetric quantity is the Computed Tomography Dose Index (CTDI). The patient sample involved 82 adult patients of both sexes and divided into three groups by their body weight (50–60 kg, 60–70 kg, 70–80 kg).Carried out the routine abdomen examinations, and all exposure parameters have been collected and the corresponding CTDIv and DLP values have been determined. The average values were compared with the European DRLs. Results: The majority of patients (75%) were between 50–70 Kg of body weight, the numbers of patient in each group of weight were 40–50:7; 50–60:29; 60–70:33; 70–80:13. The LDRLs in each group were 10.81mGy, 14.46mGy, 20.27mGy and 21.04mGy, respectively. The DLP were 477mGy, 630mGy, 887mGy and 959mGy, respectively. No matter which group the LDRLs were lower than European DRLs. Conclusions: We would like to state that this was a pioneer work in local hospital in Chiayi. We hope that this may lead the way to further developments in Taiwan.

  7. Retro-regression--another important multivariate regression improvement.

    Science.gov (United States)

    Randić, M

    2001-01-01

    We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

  8. Modified Regression Correlation Coefficient for Poisson Regression Model

    Science.gov (United States)

    Kaengthong, Nattacha; Domthong, Uthumporn

    2017-09-01

    This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

  9. A computer tool for a minimax criterion in binary response and heteroscedastic simple linear regression models.

    Science.gov (United States)

    Casero-Alonso, V; López-Fidalgo, J; Torsney, B

    2017-01-01

    Binary response models are used in many real applications. For these models the Fisher information matrix (FIM) is proportional to the FIM of a weighted simple linear regression model. The same is also true when the weight function has a finite integral. Thus, optimal designs for one binary model are also optimal for the corresponding weighted linear regression model. The main objective of this paper is to provide a tool for the construction of MV-optimal designs, minimizing the maximum of the variances of the estimates, for a general design space. MV-optimality is a potentially difficult criterion because of its nondifferentiability at equal variance designs. A methodology for obtaining MV-optimal designs where the design space is a compact interval [a, b] will be given for several standard weight functions. The methodology will allow us to build a user-friendly computer tool based on Mathematica to compute MV-optimal designs. Some illustrative examples will show a representation of MV-optimal designs in the Euclidean plane, taking a and b as the axes. The applet will be explained using two relevant models. In the first one the case of a weighted linear regression model is considered, where the weight function is directly chosen from a typical family. In the second example a binary response model is assumed, where the probability of the outcome is given by a typical probability distribution. Practitioners can use the provided applet to identify the solution and to know the exact support points and design weights. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  10. Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels

    KAUST Repository

    Wang, Xiaolei

    2014-12-12

    Background: A quantitative understanding of interactions between transcription factors (TFs) and their DNA binding sites is key to the rational design of gene regulatory networks. Recent advances in high-throughput technologies have enabled high-resolution measurements of protein-DNA binding affinity. Importantly, such experiments revealed the complex nature of TF-DNA interactions, whereby the effects of nucleotide changes on the binding affinity were observed to be context dependent. A systematic method to give high-quality estimates of such complex affinity landscapes is, thus, essential to the control of gene expression and the advance of synthetic biology. Results: Here, we propose a two-round prediction method that is based on support vector regression (SVR) with weighted degree (WD) kernels. In the first round, a WD kernel with shifts and mismatches is used with SVR to detect the importance of subsequences with different lengths at different positions. The subsequences identified as important in the first round are then fed into a second WD kernel to fit the experimentally measured affinities. To our knowledge, this is the first attempt to increase the accuracy of the affinity prediction by applying two rounds of string kernels and by identifying a small number of crucial k-mers. The proposed method was tested by predicting the binding affinity landscape of Gcn4p in Saccharomyces cerevisiae using datasets from HiTS-FLIP. Our method explicitly identified important subsequences and showed significant performance improvements when compared with other state-of-the-art methods. Based on the identified important subsequences, we discovered two surprisingly stable 10-mers and one sensitive 10-mer which were not reported before. Further test on four other TFs in S. cerevisiae demonstrated the generality of our method. Conclusion: We proposed in this paper a two-round method to quantitatively model the DNA binding affinity landscape. Since the ability to modify

  11. Modeling The Skeleton Weight of an Adult Caucasian Man.

    Science.gov (United States)

    Avtandilashvili, Maia; Tolmachev, Sergei Y

    2018-05-17

    The reference value for the skeleton weight of an adult male (10.5 kg) recommended by the International Commission on Radiological Protection in Publication 70 is based on weights of dissected skeletons from 44 individuals, including two U.S. Transuranium and Uranium Registries whole-body donors. The International Commission on Radiological Protection analysis of anatomical data from 31 individuals with known values of body height demonstrated significant correlation between skeleton weight and body height. The corresponding regression equation, Wskel (kg) = -10.7 + 0.119 × H (cm), published in International Commission on Radiological Protection Publication 70 is typically used to estimate the skeleton weight from body height. Currently, the U.S. Transuranium and Uranium Registries holds data on individual bone weights from a total of 40 male whole-body donors, which has provided a unique opportunity to update the International Commission on Radiological Protection skeleton weight vs. body height equation. The original International Commission on Radiological Protection Publication 70 and the new U.S. Transuranium and Uranium Registries data were combined in a set of 69 data points representing a group of 33- to 95-y-old individuals with body heights and skeleton weights ranging from 155 to 188 cm and 6.5 to 13.4 kg, respectively. Data were fitted with a linear least-squares regression. A significant correlation between the two parameters was observed (r = 0.28), and an updated skeleton weight vs. body height equation was derived: Wskel (kg) = -6.5 + 0.093 × H (cm). In addition, a correlation of skeleton weight with multiple variables including body height, body weight, and age was evaluated using multiple regression analysis, and a corresponding fit equation was derived: Wskel (kg) = -0.25 + 0.046 × H (cm) + 0.036 × Wbody (kg) - 0.012 × A (y). These equations will be used to estimate skeleton weights and, ultimately, total skeletal actinide activities for

  12. Geographically Weighted Regression Models in Estimating Median Home Prices in Towns of Massachusetts Based on an Urban Sustainability Framework

    Directory of Open Access Journals (Sweden)

    Yaxiong Ma

    2018-03-01

    Full Text Available Housing is a key component of urban sustainability. The objective of this study was to assess the significance of key spatial determinants of median home price in towns in Massachusetts that impact sustainable growth. Our analysis investigates the presence or absence of spatial non-stationarity in the relationship between sustainable growth, measured in terms of the relationship between home values and various parameters including the amount of unprotected forest land, residential land, unemployment, education, vehicle ownership, accessibility to commuter rail stations, school district performance, and senior population. We use the standard geographically weighted regression (GWR and Mixed GWR models to analyze the effects of spatial non-stationarity. Mixed GWR performed better than GWR in terms of Akaike Information Criterion (AIC values. Our findings highlight the nature and spatial extent of the non-stationary vs. stationary qualities of key environmental and social determinants of median home price. Understanding the key determinants of housing values, such as valuation of green spaces, public school performance metrics, and proximity to public transport, enable towns to use different strategies of sustainable urban planning, while understanding urban housing determinants—such as unemployment and senior population—can help modify urban sustainable housing policies.

  13. Least median of squares and iteratively re-weighted least squares as robust linear regression methods for fluorimetric determination of α-lipoic acid in capsules in ideal and non-ideal cases of linearity.

    Science.gov (United States)

    Korany, Mohamed A; Gazy, Azza A; Khamis, Essam F; Ragab, Marwa A A; Kamal, Miranda F

    2018-03-26

    This study outlines two robust regression approaches, namely least median of squares (LMS) and iteratively re-weighted least squares (IRLS) to investigate their application in instrument analysis of nutraceuticals (that is, fluorescence quenching of merbromin reagent upon lipoic acid addition). These robust regression methods were used to calculate calibration data from the fluorescence quenching reaction (∆F and F-ratio) under ideal or non-ideal linearity conditions. For each condition, data were treated using three regression fittings: Ordinary Least Squares (OLS), LMS and IRLS. Assessment of linearity, limits of detection (LOD) and quantitation (LOQ), accuracy and precision were carefully studied for each condition. LMS and IRLS regression line fittings showed significant improvement in correlation coefficients and all regression parameters for both methods and both conditions. In the ideal linearity condition, the intercept and slope changed insignificantly, but a dramatic change was observed for the non-ideal condition and linearity intercept. Under both linearity conditions, LOD and LOQ values after the robust regression line fitting of data were lower than those obtained before data treatment. The results obtained after statistical treatment indicated that the linearity ranges for drug determination could be expanded to lower limits of quantitation by enhancing the regression equation parameters after data treatment. Analysis results for lipoic acid in capsules, using both fluorimetric methods, treated by parametric OLS and after treatment by robust LMS and IRLS were compared for both linearity conditions. Copyright © 2018 John Wiley & Sons, Ltd.

  14. Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels

    KAUST Repository

    Wang, Xiaolei; Kuwahara, Hiroyuki; Gao, Xin

    2014-01-01

    high-quality estimates of such complex affinity landscapes is, thus, essential to the control of gene expression and the advance of synthetic biology. Results: Here, we propose a two-round prediction method that is based on support vector regression

  15. WeightLifter: Visual Weight Space Exploration for Multi-Criteria Decision Making.

    Science.gov (United States)

    Pajer, Stephan; Streit, Marc; Torsney-Weir, Thomas; Spechtenhauser, Florian; Muller, Torsten; Piringer, Harald

    2017-01-01

    A common strategy in Multi-Criteria Decision Making (MCDM) is to rank alternative solutions by weighted summary scores. Weights, however, are often abstract to the decision maker and can only be set by vague intuition. While previous work supports a point-wise exploration of weight spaces, we argue that MCDM can benefit from a regional and global visual analysis of weight spaces. Our main contribution is WeightLifter, a novel interactive visualization technique for weight-based MCDM that facilitates the exploration of weight spaces with up to ten criteria. Our technique enables users to better understand the sensitivity of a decision to changes of weights, to efficiently localize weight regions where a given solution ranks high, and to filter out solutions which do not rank high enough for any plausible combination of weights. We provide a comprehensive requirement analysis for weight-based MCDM and describe an interactive workflow that meets these requirements. For evaluation, we describe a usage scenario of WeightLifter in automotive engineering and report qualitative feedback from users of a deployed version as well as preliminary feedback from decision makers in multiple domains. This feedback confirms that WeightLifter increases both the efficiency of weight-based MCDM and the awareness of uncertainty in the ultimate decisions.

  16. TasselNet: counting maize tassels in the wild via local counts regression network.

    Science.gov (United States)

    Lu, Hao; Cao, Zhiguo; Xiao, Yang; Zhuang, Bohan; Shen, Chunhua

    2017-01-01

    Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment. With 361 field images collected in four experimental fields across China between 2010 and 2015 and corresponding manually-labelled dotted annotations, a novel Maize Tassels Counting ( MTC ) dataset is created and will be released with this paper. To alleviate the in-field challenges, a deep convolutional neural network-based approach termed TasselNet is proposed. TasselNet can achieve good adaptability to in-field variations via modelling the local visual characteristics of field images and regressing the local counts of maize tassels. Extensive results on the MTC dataset demonstrate that TasselNet outperforms other state-of-the-art approaches by large margins and achieves the overall best counting

  17. TasselNet: counting maize tassels in the wild via local counts regression network

    Directory of Open Access Journals (Sweden)

    Hao Lu

    2017-11-01

    Full Text Available Abstract Background Accurately counting maize tassels is important for monitoring the growth status of maize plants. This tedious task, however, is still mainly done by manual efforts. In the context of modern plant phenotyping, automating this task is required to meet the need of large-scale analysis of genotype and phenotype. In recent years, computer vision technologies have experienced a significant breakthrough due to the emergence of large-scale datasets and increased computational resources. Naturally image-based approaches have also received much attention in plant-related studies. Yet a fact is that most image-based systems for plant phenotyping are deployed under controlled laboratory environment. When transferring the application scenario to unconstrained in-field conditions, intrinsic and extrinsic variations in the wild pose great challenges for accurate counting of maize tassels, which goes beyond the ability of conventional image processing techniques. This calls for further robust computer vision approaches to address in-field variations. Results This paper studies the in-field counting problem of maize tassels. To our knowledge, this is the first time that a plant-related counting problem is considered using computer vision technologies under unconstrained field-based environment. With 361 field images collected in four experimental fields across China between 2010 and 2015 and corresponding manually-labelled dotted annotations, a novel Maize Tassels Counting (MTC dataset is created and will be released with this paper. To alleviate the in-field challenges, a deep convolutional neural network-based approach termed TasselNet is proposed. TasselNet can achieve good adaptability to in-field variations via modelling the local visual characteristics of field images and regressing the local counts of maize tassels. Extensive results on the MTC dataset demonstrate that TasselNet outperforms other state-of-the-art approaches by large

  18. Spatial stochastic regression modelling of urban land use

    International Nuclear Information System (INIS)

    Arshad, S H M; Jaafar, J; Abiden, M Z Z; Latif, Z A; Rasam, A R A

    2014-01-01

    Urbanization is very closely linked to industrialization, commercialization or overall economic growth and development. This results in innumerable benefits of the quantity and quality of the urban environment and lifestyle but on the other hand contributes to unbounded development, urban sprawl, overcrowding and decreasing standard of living. Regulation and observation of urban development activities is crucial. The understanding of urban systems that promotes urban growth are also essential for the purpose of policy making, formulating development strategies as well as development plan preparation. This study aims to compare two different stochastic regression modeling techniques for spatial structure models of urban growth in the same specific study area. Both techniques will utilize the same datasets and their results will be analyzed. The work starts by producing an urban growth model by using stochastic regression modeling techniques namely the Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR). The two techniques are compared to and it is found that, GWR seems to be a more significant stochastic regression model compared to OLS, it gives a smaller AICc (Akaike's Information Corrected Criterion) value and its output is more spatially explainable

  19. Meta-regression analysis to evaluate relationships between maternal blood levels of placentation biomarkers and low delivery weight.

    Science.gov (United States)

    Goto, Eita

    2018-05-03

    Caution is required for women at increased risk of low neonatal delivery weight. To evaluate relationships between maternal placentation biomarkers and the odds of low delivery weight. Databases including PubMed/MEDLINE were searched up to May 2017 using keywords involving biomarker names and "low birthweight." English language studies providing true- and false-positive, and true- and false-negative results of low delivery weight classified by maternal blood levels of placentation biomarkers (in units of multiple of the mean [MoM]) were included. Coefficients representing changes in log odds ratio for low delivery weight per 1 MoM increase in maternal blood placentation biomarkers, and those adjusted for race, sampling period, and/or study quality were calculated. Adjusted coefficients representing changes in log odds ratio for low delivery weight per 1 MoM increase in maternal blood levels of α-fetoprotein (AFP) and β-human chorionic gonadotropin (β-hCG) were significantly greater than 0 (both Plow delivery weight. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  20. Predictive factors of esophageal stenosis associated with tumor regression in radiation therapy for locally advanced esophageal cancer

    International Nuclear Information System (INIS)

    Atsumi, Kazushige; Shioyama, Yoshiyuki; Nakamura, Katsumasa

    2010-01-01

    The purpose of this retrospective study was to clarify the predictive factors correlated with esophageal stenosis within three months after radiation therapy for locally advanced esophageal cancer. We enrolled 47 patients with advanced esophageal cancer with T2-4 and stage II-III who were treated with definitive radiation therapy and achieving complete response of primary lesion at Kyushu University Hospital between January 1998 and December 2005. Esophagography was performed for all patients before treatment and within three months after completion of the radiation therapy, the esophageal stenotic ratio was evaluated. The stenotic ratio was used to define four levels of stenosis: stenosis level 1, stenotic ratio of 0-25%; 2, 25-50%; 3, 50-75%; 4, 75-100%. We then estimated the correlation between the esophageal stenosis level after radiation therapy and each of numerous factors. The numbers and total percentages of patients at each stenosis level were as follows: level 1: n=14 (30%); level 2: 8 (17%); level 3: 14 (30%); and level 4: 11 (23%). Esophageal stenosis in the case of full circumference involvement tended to be more severe and more frequent. Increases in wall thickness tended to be associated with increases in esophageal stenosis severity and frequency. The extent of involved circumference and wall thickness of tumor region were significantly correlated with esophageal stenosis associated with tumor regression in radiation therapy (p=0.0006, p=0.005). For predicting the possibility of esophageal stenosis with tumor regression within three months in radiation therapy, the extent of involved circumference and esophageal wall thickness of the tumor region may be useful. (author)

  1. Aortic and Hepatic Contrast Enhancement During Hepatic-Arterial and Portal Venous Phase Computed Tomography Scanning: Multivariate Linear Regression Analysis Using Age, Sex, Total Body Weight, Height, and Cardiac Output.

    Science.gov (United States)

    Masuda, Takanori; Nakaura, Takeshi; Funama, Yoshinori; Higaki, Toru; Kiguchi, Masao; Imada, Naoyuki; Sato, Tomoyasu; Awai, Kazuo

    We evaluated the effect of the age, sex, total body weight (TBW), height (HT) and cardiac output (CO) of patients on aortic and hepatic contrast enhancement during hepatic-arterial phase (HAP) and portal venous phase (PVP) computed tomography (CT) scanning. This prospective study received institutional review board approval; prior informed consent to participate was obtained from all 168 patients. All were examined using our routine protocol; the contrast material was 600 mg/kg iodine. Cardiac output was measured with a portable electrical velocimeter within 5 minutes of starting the CT scan. We calculated contrast enhancement (per gram of iodine: [INCREMENT]HU/gI) of the abdominal aorta during the HAP and of the liver parenchyma during the PVP. We performed univariate and multivariate linear regression analysis between all patient characteristics and the [INCREMENT]HU/gI of aortic- and liver parenchymal enhancement. Univariate linear regression analysis demonstrated statistically significant correlations between the [INCREMENT]HU/gI and the age, sex, TBW, HT, and CO (all P linear regression analysis showed that only the TBW and CO were of independent predictive value (P linear regression analysis only the TBW and CO were significantly correlated with aortic and liver parenchymal enhancement; the age, sex, and HT were not. The CO was the only independent factor affecting aortic and liver parenchymal enhancement at hepatic CT when the protocol was adjusted for the TBW.

  2. Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

    Directory of Open Access Journals (Sweden)

    Laermans Eric

    2011-01-01

    Full Text Available Abstract Received signal strength indication (RSSI-based localization is emerging in wireless sensor networks (WSNs. Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure.

  3. Effect of psychological distress on weight concern and weight control behaviors.

    Science.gov (United States)

    Roohafza, Hamidreza; Kabir, Ali; Sadeghi, Masoumeh; Shokouh, Pedram; Aalaei-Andabili, Seyed Hossein; Mehrabi, Yadollah; Sarrafzadegan, Nizal

    2014-09-01

    Obesity is associated with chronic disorders like coronary artery diseases, metabolic syndrome, cancers, and psychiatric disorders. Stress may contribute to weight gain by disrupting weight concern, and lead to uncontrolled eating behavior. This study aimed to investigate the effects of stress on weight concern and control behaviors in normal weight and obese adults. A total of 9544 subjects were selected by multi-stage random sampling from three provinces in central Iran. Information related to weight concern and control behavior was registered in normal weight and obese participants. Psychological distress was measured by a 12-item General Health Questionnaire (GHQ-12) and subjects were divided into high and low stress groups. Logistic regression was used for analysis. The mean age of participants was 38.7 ± 15.5 years and 50% (4772) of them were males. The adjusted odds ratio (OR) for age, sex and education of high stress to low stress level for weight concern, weight control behavior and acceptable physical activity behavior was more than 1; but the OR was less than 1 for waist circumference, obesity and healthy diet behavior. Among obese participants, higher levels of stress were associated with lower weight concern with OR, 95%CI: 0.821, (0.682 - 0.988), lower acceptable physical activity with OR = 0.833, 95%CI: (0.624 - 0.912), but higher rates of healthy diet behavior with OR = 1.360, 95% CI: (1.040 - 1.780). Individuals with high stress level have lower weight concern and lower physical activity; therefore, they are prone to weight gain and obesity. It could be concluded that stress management should be considered as a crucial component of obesity prevention and control programs.

  4. On the null distribution of Bayes factors in linear regression

    Science.gov (United States)

    We show that under the null, the 2 log (Bayes factor) is asymptotically distributed as a weighted sum of chi-squared random variables with a shifted mean. This claim holds for Bayesian multi-linear regression with a family of conjugate priors, namely, the normal-inverse-gamma prior, the g-prior, and...

  5. Mapping geogenic radon potential by regression kriging

    Energy Technology Data Exchange (ETDEWEB)

    Pásztor, László [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Szabó, Katalin Zsuzsanna, E-mail: sz_k_zs@yahoo.de [Department of Chemistry, Institute of Environmental Science, Szent István University, Páter Károly u. 1, Gödöllő 2100 (Hungary); Szatmári, Gábor; Laborczi, Annamária [Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Hungarian Academy of Sciences, Department of Environmental Informatics, Herman Ottó út 15, 1022 Budapest (Hungary); Horváth, Ákos [Department of Atomic Physics, Eötvös University, Pázmány Péter sétány 1/A, 1117 Budapest (Hungary)

    2016-02-15

    Radon ({sup 222}Rn) gas is produced in the radioactive decay chain of uranium ({sup 238}U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method

  6. Mapping geogenic radon potential by regression kriging

    International Nuclear Information System (INIS)

    Pásztor, László; Szabó, Katalin Zsuzsanna; Szatmári, Gábor; Laborczi, Annamária; Horváth, Ákos

    2016-01-01

    Radon ( 222 Rn) gas is produced in the radioactive decay chain of uranium ( 238 U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly. - Highlights: • A new method, regression

  7. Disease Human - MDC_LowBirthWeight

    Data.gov (United States)

    NSGIC Local Govt | GIS Inventory — Polygon feature class based on Zip Code boundaries showing the percentage of babies born in Miami-Dade County in 2006 with low birth weights. Low birth weight is...

  8. Application of nonparametric regression methods to study the relationship between NO2 concentrations and local wind direction and speed at background sites.

    Science.gov (United States)

    Donnelly, Aoife; Misstear, Bruce; Broderick, Brian

    2011-02-15

    Background concentrations of nitrogen dioxide (NO(2)) are not constant but vary temporally and spatially. The current paper presents a powerful tool for the quantification of the effects of wind direction and wind speed on background NO(2) concentrations, particularly in cases where monitoring data are limited. In contrast to previous studies which applied similar methods to sites directly affected by local pollution sources, the current study focuses on background sites with the aim of improving methods for predicting background concentrations adopted in air quality modelling studies. The relationship between measured NO(2) concentration in air at three such sites in Ireland and locally measured wind direction has been quantified using nonparametric regression methods. The major aim was to analyse a method for quantifying the effects of local wind direction on background levels of NO(2) in Ireland. The method was expanded to include wind speed as an added predictor variable. A Gaussian kernel function is used in the analysis and circular statistics employed for the wind direction variable. Wind direction and wind speed were both found to have a statistically significant effect on background levels of NO(2) at all three sites. Frequently environmental impact assessments are based on short term baseline monitoring producing a limited dataset. The presented non-parametric regression methods, in contrast to the frequently used methods such as binning of the data, allow concentrations for missing data pairs to be estimated and distinction between spurious and true peaks in concentrations to be made. The methods were found to provide a realistic estimation of long term concentration variation with wind direction and speed, even for cases where the data set is limited. Accurate identification of the actual variation at each location and causative factors could be made, thus supporting the improved definition of background concentrations for use in air quality modelling

  9. Lokální polynomická regrese

    OpenAIRE

    Cigán, Martin

    2015-01-01

    This thesis examines local polynomial regression. Local polynomial regression is one of non-parametric approach of data fitting. This particular method is based on repetition of fitting data using weighted least squares estimate of the parameters of the polynomial model. The aim of this thesis is therefore revision of some properties of the weighted least squares estimate used in linear regression model and introduction of the non-robust method of local polynomial regression. Some statistical...

  10. Randomizing world trade. II. A weighted network analysis

    Science.gov (United States)

    Squartini, Tiziano; Fagiolo, Giorgio; Garlaschelli, Diego

    2011-10-01

    Based on the misleading expectation that weighted network properties always offer a more complete description than purely topological ones, current economic models of the International Trade Network (ITN) generally aim at explaining local weighted properties, not local binary ones. Here we complement our analysis of the binary projections of the ITN by considering its weighted representations. We show that, unlike the binary case, all possible weighted representations of the ITN (directed and undirected, aggregated and disaggregated) cannot be traced back to local country-specific properties, which are therefore of limited informativeness. Our two papers show that traditional macroeconomic approaches systematically fail to capture the key properties of the ITN. In the binary case, they do not focus on the degree sequence and hence cannot characterize or replicate higher-order properties. In the weighted case, they generally focus on the strength sequence, but the knowledge of the latter is not enough in order to understand or reproduce indirect effects.

  11. Measurement of lower limb alignment: there are within-person differences between weight-bearing and non-weight-bearing measurement modalities.

    Science.gov (United States)

    Schoenmakers, Daphne A L; Feczko, Peter Z; Boonen, Bert; Schotanus, Martijn G M; Kort, Nanne P; Emans, Pieter J

    2017-11-01

    Previous studies have compared weight-bearing mechanical leg axis (MLA) measurements to non-weight-bearing measurement modalities. Most of these studies compared mean or median values and did not analyse within-person differences between measurements. This study evaluates the within-person agreement of MLA measurements between weight-bearing full-length radiographs (FLR) and non-weight-bearing measurement modalities (computer-assisted surgery (CAS) navigation or MRI). Two independent observers measured the MLA on pre- and postoperative weight-bearing FLR in 168 patients. These measurements were compared to non-weight-bearing measurements obtained by CAS navigation or MRI. Absolute differences in individual subjects were calculated to determine the agreement between measurement modalities. Linear regression was used to evaluate the possibility that other independent variables impact the differences in measurements. A difference was found in preoperative measurements between FLR and CAS navigation (mean of 2.5° with limit of agreement (1.96 SD) of 6.4°), as well as between FLR and MRI measurements (mean of 2.4° with limit of agreement (1.96 SD) of 6.9°). Postoperatively, the mean difference between MLA measured on FLR compared to CAS navigation was 1.5° (limit of agreement (1.96 SD) of 4.6°). Linear regression analysis showed that weight-bearing MLA measurements vary significantly from non-weight-bearing MLA measurements. Differences were more severe in patients with mediolateral instability (p = 0.010), age (p = 0.049) and ≥3° varus or valgus alignment (p = 0.008). The clinical importance of this study lies in the finding that there are within-person differences between weight-bearing and non-weight-bearing measurement modalities. This has implications for preoperative planning, performing total knee arthroplasty (TKA), and clinical follow-up after TKA surgery using CAS navigation or patient-specific instrumentation. III.

  12. Retrieving relevant factors with exploratory SEM and principal-covariate regression: A comparison.

    Science.gov (United States)

    Vervloet, Marlies; Van den Noortgate, Wim; Ceulemans, Eva

    2018-02-12

    Behavioral researchers often linearly regress a criterion on multiple predictors, aiming to gain insight into the relations between the criterion and predictors. Obtaining this insight from the ordinary least squares (OLS) regression solution may be troublesome, because OLS regression weights show only the effect of a predictor on top of the effects of other predictors. Moreover, when the number of predictors grows larger, it becomes likely that the predictors will be highly collinear, which makes the regression weights' estimates unstable (i.e., the "bouncing beta" problem). Among other procedures, dimension-reduction-based methods have been proposed for dealing with these problems. These methods yield insight into the data by reducing the predictors to a smaller number of summarizing variables and regressing the criterion on these summarizing variables. Two promising methods are principal-covariate regression (PCovR) and exploratory structural equation modeling (ESEM). Both simultaneously optimize reduction and prediction, but they are based on different frameworks. The resulting solutions have not yet been compared; it is thus unclear what the strengths and weaknesses are of both methods. In this article, we focus on the extents to which PCovR and ESEM are able to extract the factors that truly underlie the predictor scores and can predict a single criterion. The results of two simulation studies showed that for a typical behavioral dataset, ESEM (using the BIC for model selection) in this regard is successful more often than PCovR. Yet, in 93% of the datasets PCovR performed equally well, and in the case of 48 predictors, 100 observations, and large differences in the strengths of the factors, PCovR even outperformed ESEM.

  13. A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks.

    Science.gov (United States)

    Li, Xinbin; Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping

    2017-12-21

    Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid "particle degeneracy" problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.

  14. Prediksi Bobot dan Konformasi Karkas Kambing Lokal Mengunakan Prediktor Bobot Potong dengan Berbagai Model Regresi

    Directory of Open Access Journals (Sweden)

    Akhmad Sodiq

    2011-10-01

    Full Text Available Prediction for carcass weight and conformation of local goat by slaughter weight predictor using some regression models ABSTRACT. The goat population of Indonesia is concentrated in Central Java province especially under smallholder farming areas, and mostly their function is the production of meat. Local breed (Jawa Randu and Peranakan Etawah Crossbred are very common raised by samallholders in Banyumas areas. The local kids are raised with their mothers and slaughtered after post weaning (6–8 months old. Carcass characteristics are important criteria for consumers and it could be taking into account. The objective of this study was to estimate the carcass weight and conformation of local goat by predictor of slaughter weight using some regression models. Eighty male of local goats (Peranakan Etawah and Jawa Randu crossbred, body weight ranged from 10-23.5 kg (6-8 months of age resulted from village production system were used in this study. Carcass weight, dressing percentage, and carcass conformation were recorded. Ten models of estimation curve procedure were applied in terms of linear and nonlinear regression models. The analysis display relation between slaughter weight (X and carcass weight and conformation (Y. The higher of determination coefficient (r2 and the lowest of the standard error means (M.SE was found in the power regression model. Carcass weight of local goat (Y could be effectively assessed by slaughter weight (X using power regression model Y= 0.593907 (X 0,893021 or ln (Y = ln (0.593907 + 0,893021 ln(X; and conformation carcass (Y could be effectively predicted by slaughter weight (X using power regression model Y= 14.995466 (X 0,267867 or ln (Y = ln (14.995466 + 0,267867 ln (X.

  15. An automated tool for cortical feature analysis: Application to differences on 7 Tesla T2* -weighted images between young and older healthy subjects.

    Science.gov (United States)

    Doan, Nhat Trung; van Rooden, Sanneke; Versluis, Maarten J; Buijs, Mathijs; Webb, Andrew G; van der Grond, Jeroen; van Buchem, Mark A; Reiber, Johan H C; Milles, Julien

    2015-07-01

    High field T 2 * -weighted MR images of the cerebral cortex are increasingly used to study tissue susceptibility changes related to aging or pathologies. This paper presents a novel automated method for the computation of quantitative cortical measures and group-wise comparison using 7 Tesla T 2 * -weighted magnitude and phase images. The cerebral cortex was segmented using a combination of T 2 * -weighted magnitude and phase information and subsequently was parcellated based on an anatomical atlas. Local gray matter (GM)/white matter (WM) contrast and cortical profiles, which depict the magnitude or phase variation across the cortex, were computed from the magnitude and phase images in each parcellated region and further used for group-wise comparison. Differences in local GM/WM contrast were assessed using linear regression analysis. Regional cortical profiles were compared both globally and locally using permutation testing. The method was applied to compare a group of 10 young volunteers with a group of 15 older subjects. Using local GM/WM contrast, significant differences were revealed in at least 13 of 17 studied regions. Highly significant differences between cortical profiles were shown in all regions. The proposed method can be a useful tool for studying cortical changes in normal aging and potentially in neurodegenerative diseases. Magn Reson Med 74:240-248, 2015. © 2014 Wiley Periodicals, Inc. © 2014 Wiley Periodicals, Inc.

  16. Genetic evaluation of European quails by random regression models

    Directory of Open Access Journals (Sweden)

    Flaviana Miranda Gonçalves

    2012-09-01

    Full Text Available The objective of this study was to compare different random regression models, defined from different classes of heterogeneity of variance combined with different Legendre polynomial orders for the estimate of (covariance of quails. The data came from 28,076 observations of 4,507 female meat quails of the LF1 lineage. Quail body weights were determined at birth and 1, 14, 21, 28, 35 and 42 days of age. Six different classes of residual variance were fitted to Legendre polynomial functions (orders ranging from 2 to 6 to determine which model had the best fit to describe the (covariance structures as a function of time. According to the evaluated criteria (AIC, BIC and LRT, the model with six classes of residual variances and of sixth-order Legendre polynomial was the best fit. The estimated additive genetic variance increased from birth to 28 days of age, and dropped slightly from 35 to 42 days. The heritability estimates decreased along the growth curve and changed from 0.51 (1 day to 0.16 (42 days. Animal genetic and permanent environmental correlation estimates between weights and age classes were always high and positive, except for birth weight. The sixth order Legendre polynomial, along with the residual variance divided into six classes was the best fit for the growth rate curve of meat quails; therefore, they should be considered for breeding evaluation processes by random regression models.

  17. Gender and Socioeconomic Status in Relation to Weight Perception and Weight Control Behavior in Korean Adults

    Directory of Open Access Journals (Sweden)

    Hee-Kyung Joh

    2013-02-01

    Full Text Available Aim: In Korea, obesity is more prevalent among men and lower socioeconomic groups. To explain this obesity disparity, we compared weight perception and weight control behavior across gender and socioeconomic status (SES. Methods: We analyzed data from 16,260 participants aged 20 years or older in a nationally representative cross-sectional survey. SES indicators included education and income levels. Weight under-perception was defined when participants considered themselves lighter than their measured BMI status. Either no active or inappropriate weight control (i.e., trying to gain weight in obese individuals was considered to be unhealthy patterns. Multivariate prevalence ratios were calculated using log-binomial regressions. Results: Men had a higher prevalence of weight under-perception (24.5 vs. 11.9% and unhealthy patterns of weight control behavior (57 vs. 40% than women. Low education level was associated with weight under-perception (ptrend = 0.022 in men, ptrend trend trend = 0.047 in men, ptrend Conclusion: Weight perception and weight control behavior significantly varied by gender and SES. Public actions should be directed toward improving perception and behavior of high-risk populations.

  18. Computing group cardinality constraint solutions for logistic regression problems.

    Science.gov (United States)

    Zhang, Yong; Kwon, Dongjin; Pohl, Kilian M

    2017-01-01

    We derive an algorithm to directly solve logistic regression based on cardinality constraint, group sparsity and use it to classify intra-subject MRI sequences (e.g. cine MRIs) of healthy from diseased subjects. Group cardinality constraint models are often applied to medical images in order to avoid overfitting of the classifier to the training data. Solutions within these models are generally determined by relaxing the cardinality constraint to a weighted feature selection scheme. However, these solutions relate to the original sparse problem only under specific assumptions, which generally do not hold for medical image applications. In addition, inferring clinical meaning from features weighted by a classifier is an ongoing topic of discussion. Avoiding weighing features, we propose to directly solve the group cardinality constraint logistic regression problem by generalizing the Penalty Decomposition method. To do so, we assume that an intra-subject series of images represents repeated samples of the same disease patterns. We model this assumption by combining series of measurements created by a feature across time into a single group. Our algorithm then derives a solution within that model by decoupling the minimization of the logistic regression function from enforcing the group sparsity constraint. The minimum to the smooth and convex logistic regression problem is determined via gradient descent while we derive a closed form solution for finding a sparse approximation of that minimum. We apply our method to cine MRI of 38 healthy controls and 44 adult patients that received reconstructive surgery of Tetralogy of Fallot (TOF) during infancy. Our method correctly identifies regions impacted by TOF and generally obtains statistically significant higher classification accuracy than alternative solutions to this model, i.e., ones relaxing group cardinality constraints. Copyright © 2016 Elsevier B.V. All rights reserved.

  19. Investigating DRG cost weights for hospitals in middle income countries.

    Science.gov (United States)

    Ghaffari, Shahram; Doran, Christopher; Wilson, Andrew; Aisbett, Chris; Jackson, Terri

    2009-01-01

    Identifying the cost of hospital outputs, particularly acute inpatients measured by Diagnosis Related Groups (DRGs), is an important component of casemix implementation. Measuring the relative costliness of specific DRGs is useful for a wide range of policy and planning applications. Estimating the relative use of resources per DRG can be done through different costing approaches depending on availability of information and time and budget. This study aims to guide costing efforts in Iran and other countries in the region that are pursuing casemix funding, through identifying the main issues facing cost finding approaches and introducing the costing models compatible with their hospitals accounting and management structures. The results show that inadequate financial and utilisation information at the patient's level, poorly computerized 'feeder systems'; and low quality data make it impossible to estimate reliable DRGs costs through clinical costing. A cost modelling approach estimates the average cost of 2.723 million Rials (Iranian Currency) per DRG. Using standard linear regression, a coefficient of 0.14 (CI = 0.12-0.16) suggests that the average cost weight increases by 14% for every one-day increase in average length of stay (LOS).We concluded that calculation of DRG cost weights (CWs) using Australian service weights provides a sensible starting place for DRG-based hospital management; but restructuring hospital accounting systems, designing computerized feeder systems, using appropriate software, and development of national service weights that reflect local practice patterns will enhance the accuracy of DRG CWs.

  20. Dual Regression

    OpenAIRE

    Spady, Richard; Stouli, Sami

    2012-01-01

    We propose dual regression as an alternative to the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provides all the interpretational power of the quantile regression process while avoiding the need for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our approach introduces a mathematical programming characterization of conditional distribution f...

  1. Physical Activity for a Healthy Weight

    Science.gov (United States)

    ... Weight Breastfeeding Micronutrient Malnutrition State and Local Programs Physical Activity for a Healthy Weight Language: English Español (Spanish) ... calories are used in typical activities? Why is physical activity important? Regular physical activity is important for good ...

  2. Using data mining to predict success in a weight loss trial.

    Science.gov (United States)

    Batterham, M; Tapsell, L; Charlton, K; O'Shea, J; Thorne, R

    2017-08-01

    Traditional methods for predicting weight loss success use regression approaches, which make the assumption that the relationships between the independent and dependent (or logit of the dependent) variable are linear. The aim of the present study was to investigate the relationship between common demographic and early weight loss variables to predict weight loss success at 12 months without making this assumption. Data mining methods (decision trees, generalised additive models and multivariate adaptive regression splines), in addition to logistic regression, were employed to predict: (i) weight loss success (defined as ≥5%) at the end of a 12-month dietary intervention using demographic variables [body mass index (BMI), sex and age]; percentage weight loss at 1 month; and (iii) the difference between actual and predicted weight loss using an energy balance model. The methods were compared by assessing model parsimony and the area under the curve (AUC). The decision tree provided the most clinically useful model and had a good accuracy (AUC 0.720 95% confidence interval = 0.600-0.840). Percentage weight loss at 1 month (≥0.75%) was the strongest predictor for successful weight loss. Within those individuals losing ≥0.75%, individuals with a BMI (≥27 kg m -2 ) were more likely to be successful than those with a BMI between 25 and 27 kg m -2 . Data mining methods can provide a more accurate way of assessing relationships when conventional assumptions are not met. In the present study, a decision tree provided the most parsimonious model. Given that early weight loss cannot be predicted before randomisation, incorporating this information into a post randomisation trial design may give better weight loss results. © 2017 The British Dietetic Association Ltd.

  3. Body Weight Determination from Foot Outline Length among the Iban Population in Malaysia

    Directory of Open Access Journals (Sweden)

    Hairunnisa Bt Mohd Anas K

    2017-12-01

    Full Text Available Foot impressions form a valuable physical evidence to solve crime. Foot impression measurements provide valuable information in estimating stature, weight, gender and age in crime scene investigation. In Asian countries, many people living in rural places walk without footwear. The aim of this research is to generate regression equations to determine living body weight from foot outline length among the Iban population of Malaysia. The study involved 200 (100 males, 100 females adult Ibans, mostly living in Sarawak, a state in   Malaysia. Following the standard procedure, the foot outlines were collected followed by body weight measurements and were recorded for analysis. The collected data were analysed with PASW 20 computer software. The correlation coefficient (R between the foot outline lengths and body weight was determined for males, females and pooled sample. Based on the foot outline and body weight, 30 regression equations were generated, 10 for males, 10 for females and 10 for pooled samples/unknown gender. The correlation coefficient (R values were positive and statistically significant. It is concluded that the present investigation provided regression equations to determine body weight from foot outline anthropometry. These equations can be used to determine body weight even when partial foot impressions are available at crime scenes.   Keywords: Forensic Science, Body Weight, Foot Outline, Iban Population, Malaysia

  4. Improving protein mass and cumulative body weight gain of local chicken fed ration fortified with a combination of Lactobacillus sp. and dahlia inulin

    Science.gov (United States)

    Wahyuni, H. I.; Suthama, N.; Mangisah, I.; Krismiyanto, L.

    2018-01-01

    The research aimed to evaluate meat calcium and protein content of local chicken fed diet fortified with a combination of Lactobacillus sp and Dahlia Inulin. One hundred and twenty birds of 4 months old local chicken with average body weight of 1001 g were assigned in a completely randomized design with 4 treatments and 5 replications. The treatments were the farmer formulated ration (FF) and the improved ration (IR), fortified with 1.2% inulin and 1.2 ml Lactobacillus sp. (FFIL and IRIL). Parameters were calcium retention, protein coefficient digestibility, meat calcium and protein mass, and cumulative body weight gain. The results showed that all parameters were significantly affected by dietary treatments. The improved ration resulted in higher calcium retention and protein coefficient digestibility than the farmer formulated ration when fed by both with and without fortification of dahlia inulin and Lactobacillus sp. Meat protein mass of chicken fed by both FR and IR fortified with dahlia inulin and Lactobacillus sp. showed higher value than chicken fed by unfortified FR and IR. Cumulative body weight gain of chicken fed by both FR and IR fortified with dahlia inulin and Lactobacillus sp. also showed higher value than chicken fed by without fortification. In conclusion, both FR and IR fortified with dahlia inulin and Lactobacillus sp. improved meat protein mass and cumulative body weight gain, especially the farmer formulated ration was pronouncedly improved by fortification of Lactobacillus sp. and dahlia inulin.

  5. Weight Fluctuation and Postmenopausal Breast Cancer in the National Health and Nutrition Examination Survey I Epidemiologic Follow-Up Study

    Directory of Open Access Journals (Sweden)

    Marina Komaroff

    2016-01-01

    Full Text Available Objective. The aim of this study is to investigate if weight fluctuation is an independent risk factor for postmenopausal breast cancer (PBC among women who gained weight in adult years. Methods. NHANES I Epidemiologic Follow-Up Study (NHEFS database was used in the study. Women that were cancers-free at enrollment and diagnosed for the first time with breast cancer at age 50 or greater were considered cases. Controls were chosen from the subset of cancers-free women and matched to cases by years of follow-up and status of body mass index (BMI at 25 years of age. Weight fluctuation was measured by the root-mean-square-error (RMSE from a simple linear regression model for each woman with their body mass index (BMI regressed on age (started at 25 years while women with the positive slope from this regression were defined as weight gainers. Data were analyzed using conditional logistic regression models. Results. A total of 158 women were included into the study. The conditional logistic regression adjusted for weight gain demonstrated positive association between weight fluctuation in adult years and postmenopausal breast cancers (odds ratio/OR = 1.67; 95% confidence interval/CI: 1.06–2.66. Conclusions. The data suggested that long-term weight fluctuation was significant risk factor for PBC among women who gained weight in adult years. This finding underscores the importance of maintaining lost weight and avoiding weight fluctuation.

  6. birth-weight infants

    African Journals Online (AJOL)

    including the CRIB (Clinical Risk Index for Babies) score, in a local ... these babies for expensive tertiary care. Subjects. ... patient numbers, the tendency is simply to increase the ... included birth weight, gestational age, 5-minute Apgar score ...

  7. Weight and height prediction of immobilized patients

    OpenAIRE

    Rabito,Estela Iraci; Vannucchi,Gabriela Bergamini; Suen,Vivian Marques Miguel; Castilho Neto,Laércio Lopes; Marchini,Júlio Sérgio

    2006-01-01

    OBJECTIVE: To confirm the adequacy of the formula suggested in the literature and/or to develop appropriate equations for the Brazilian population of immobilized patients based on simple anthropometric measurements. METHODS: Hospitalized patients were submitted to anthropometry and methods to estimate weight and height of bedridden patients were developed by multiple linear regression. RESULTS: Three hundred sixty eight persons were evaluated at two hospital centers and five weight-predicting...

  8. Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling study

    Directory of Open Access Journals (Sweden)

    Helen J Mayfield, PhD

    2018-05-01

    Full Text Available Summary: Background: Leptospirosis is a globally important zoonotic disease, with complex exposure pathways that depend on interactions between human beings, animals, and the environment. Major drivers of outbreaks include flooding, urbanisation, poverty, and agricultural intensification. The intensity of these drivers and their relative importance vary between geographical areas; however, non-spatial regression methods are incapable of capturing the spatial variations. This study aimed to explore the use of geographically weighted logistic regression (GWLR to provide insights into the ecoepidemiology of human leptospirosis in Fiji. Methods: We obtained field data from a cross-sectional community survey done in 2013 in the three main islands of Fiji. A blood sample obtained from each participant (aged 1–90 years was tested for anti-Leptospira antibodies and household locations were recorded using GPS receivers. We used GWLR to quantify the spatial variation in the relative importance of five environmental and sociodemographic covariates (cattle density, distance to river, poverty rate, residential setting [urban or rural], and maximum rainfall in the wettest month on leptospirosis transmission in Fiji. We developed two models, one using GWLR and one with standard logistic regression; for each model, the dependent variable was the presence or absence of anti-Leptospira antibodies. GWLR results were compared with results obtained with standard logistic regression, and used to produce a predictive risk map and maps showing the spatial variation in odds ratios (OR for each covariate. Findings: The dataset contained location information for 2046 participants from 1922 households representing 81 communities. The Aikaike information criterion value of the GWLR model was 1935·2 compared with 1254·2 for the standard logistic regression model, indicating that the GWLR model was more efficient. Both models produced similar OR for the covariates, but

  9. A method of estimating log weights.

    Science.gov (United States)

    Charles N. Mann; Hilton H. Lysons

    1972-01-01

    This paper presents a practical method of estimating the weights of logs before they are yarded. Knowledge of log weights is required to achieve optimum loading of modern yarding equipment. Truckloads of logs are weighed and measured to obtain a local density index (pounds per cubic foot) for a species of logs. The density index is then used to estimate the weights of...

  10. Adaptive Linear and Normalized Combination of Radial Basis Function Networks for Function Approximation and Regression

    Directory of Open Access Journals (Sweden)

    Yunfeng Wu

    2014-01-01

    Full Text Available This paper presents a novel adaptive linear and normalized combination (ALNC method that can be used to combine the component radial basis function networks (RBFNs to implement better function approximation and regression tasks. The optimization of the fusion weights is obtained by solving a constrained quadratic programming problem. According to the instantaneous errors generated by the component RBFNs, the ALNC is able to perform the selective ensemble of multiple leaners by adaptively adjusting the fusion weights from one instance to another. The results of the experiments on eight synthetic function approximation and six benchmark regression data sets show that the ALNC method can effectively help the ensemble system achieve a higher accuracy (measured in terms of mean-squared error and the better fidelity (characterized by normalized correlation coefficient of approximation, in relation to the popular simple average, weighted average, and the Bagging methods.

  11. Direct integral linear least square regression method for kinetic evaluation of hepatobiliary scintigraphy

    International Nuclear Information System (INIS)

    Shuke, Noriyuki

    1991-01-01

    In hepatobiliary scintigraphy, kinetic model analysis, which provides kinetic parameters like hepatic extraction or excretion rate, have been done for quantitative evaluation of liver function. In this analysis, unknown model parameters are usually determined using nonlinear least square regression method (NLS method) where iterative calculation and initial estimate for unknown parameters are required. As a simple alternative to NLS method, direct integral linear least square regression method (DILS method), which can determine model parameters by a simple calculation without initial estimate, is proposed, and tested the applicability to analysis of hepatobiliary scintigraphy. In order to see whether DILS method could determine model parameters as good as NLS method, or to determine appropriate weight for DILS method, simulated theoretical data based on prefixed parameters were fitted to 1 compartment model using both DILS method with various weightings and NLS method. The parameter values obtained were then compared with prefixed values which were used for data generation. The effect of various weights on the error of parameter estimate was examined, and inverse of time was found to be the best weight to make the error minimum. When using this weight, DILS method could give parameter values close to those obtained by NLS method and both parameter values were very close to prefixed values. With appropriate weighting, the DILS method could provide reliable parameter estimate which is relatively insensitive to the data noise. In conclusion, the DILS method could be used as a simple alternative to NLS method, providing reliable parameter estimate. (author)

  12. Trajectory modeling of gestational weight: A functional principal component analysis approach.

    Directory of Open Access Journals (Sweden)

    Menglu Che

    Full Text Available Suboptimal gestational weight gain (GWG, which is linked to increased risk of adverse outcomes for a pregnant woman and her infant, is prevalent. In the study of a large cohort of Canadian pregnant women, our goals are to estimate the individual weight growth trajectory using sparsely collected bodyweight data, and to identify the factors affecting the weight change during pregnancy, such as prepregnancy body mass index (BMI, dietary intakes and physical activity. The first goal was achieved through functional principal component analysis (FPCA by conditional expectation. For the second goal, we used linear regression with the total weight gain as the response variable. The trajectory modeling through FPCA had a significantly smaller root mean square error (RMSE and improved adaptability than the classic nonlinear mixed-effect models, demonstrating a novel tool that can be used to facilitate real time monitoring and interventions of GWG. Our regression analysis showed that prepregnancy BMI had a high predictive value for the weight changes during pregnancy, which agrees with the published weight gain guideline.

  13. Statistical approach for selection of regression model during validation of bioanalytical method

    Directory of Open Access Journals (Sweden)

    Natalija Nakov

    2014-06-01

    Full Text Available The selection of an adequate regression model is the basis for obtaining accurate and reproducible results during the bionalytical method validation. Given the wide concentration range, frequently present in bioanalytical assays, heteroscedasticity of the data may be expected. Several weighted linear and quadratic regression models were evaluated during the selection of the adequate curve fit using nonparametric statistical tests: One sample rank test and Wilcoxon signed rank test for two independent groups of samples. The results obtained with One sample rank test could not give statistical justification for the selection of linear vs. quadratic regression models because slight differences between the error (presented through the relative residuals were obtained. Estimation of the significance of the differences in the RR was achieved using Wilcoxon signed rank test, where linear and quadratic regression models were treated as two independent groups. The application of this simple non-parametric statistical test provides statistical confirmation of the choice of an adequate regression model.

  14. Serum Concentration of Leptin in Pregnant Adolescents Correlated with Gestational Weight Gain, Postpartum Weight Retention and Newborn Weight/Length

    Directory of Open Access Journals (Sweden)

    Reyna Sámano

    2017-09-01

    Full Text Available Introduction: Gestational weight gain is an important modifiable factor known to influence fetal outcomes including birth weight and adiposity. Leptin is normally correlated with adiposity and is also known to increase throughout pregnancy, as the placenta becomes a source of leptin synthesis. Several studies have reported positive correlations between cord blood leptin level and either birthweight or size for gestational age, as well as body mass index (BMI. Objective: To determine the correlation of prenatal leptin concentration in pregnant adolescents with their gestational weight gain, postpartum weight retention, and weight/length of their newborn. Methods: A cohort study was conducted on pregnant Mexican adolescents from Gestational Week 26–28 to three months postpartum (n = 168 mother–child dyads. An anthropometric assessment was made of each pregnant adolescent, and the serum level of leptin and the intake of energy were determined. The newborn was evaluated each month during postpartum. Clinical records were reviewed to obtain sociodemographic data. Bivariate correlations, tests for repeating measurements and logistic regression models were performed. Results: Leptin concentration gradually increased during the third trimester of pregnancy. At Gestation Week 36, leptin level correlated with gestational weight gain. When comparing adolescents that had the lowest and highest concentration of leptin, the former presented a mean of 6 kg less in gestational weight gain (inter-subject leptin concentration, p = 0.001; inter-subject energy intake, p = 0.497. Leptin concentration and gestational weight gain exerted an effect on the weight of the newborn (inter-subject leptin concentration for Week 32, p = 0.024; inter-subject gestational weight gain, p = 0.011. Newborn length was associated with leptin concentration at Week 28 (leptin effect, p = 0.003; effect of gestational weight gain, p = 0.722. Conclusions: Pregnant adolescents with

  15. Serum Concentration of Leptin in Pregnant Adolescents Correlated with Gestational Weight Gain, Postpartum Weight Retention and Newborn Weight/Length.

    Science.gov (United States)

    Sámano, Reyna; Martínez-Rojano, Hugo; Chico-Barba, Gabriela; Godínez-Martínez, Estela; Sánchez-Jiménez, Bernarda; Montiel-Ojeda, Diana; Tolentino, Maricruz

    2017-09-27

    Introduction : Gestational weight gain is an important modifiable factor known to influence fetal outcomes including birth weight and adiposity. Leptin is normally correlated with adiposity and is also known to increase throughout pregnancy, as the placenta becomes a source of leptin synthesis. Several studies have reported positive correlations between cord blood leptin level and either birthweight or size for gestational age, as well as body mass index (BMI). Objective : To determine the correlation of prenatal leptin concentration in pregnant adolescents with their gestational weight gain, postpartum weight retention, and weight/length of their newborn. Methods : A cohort study was conducted on pregnant Mexican adolescents from Gestational Week 26-28 to three months postpartum ( n = 168 mother-child dyads). An anthropometric assessment was made of each pregnant adolescent, and the serum level of leptin and the intake of energy were determined. The newborn was evaluated each month during postpartum. Clinical records were reviewed to obtain sociodemographic data. Bivariate correlations, tests for repeating measurements and logistic regression models were performed. Results : Leptin concentration gradually increased during the third trimester of pregnancy. At Gestation Week 36, leptin level correlated with gestational weight gain. When comparing adolescents that had the lowest and highest concentration of leptin, the former presented a mean of 6 kg less in gestational weight gain (inter-subject leptin concentration, p = 0.001; inter-subject energy intake, p = 0.497). Leptin concentration and gestational weight gain exerted an effect on the weight of the newborn (inter-subject leptin concentration for Week 32, p = 0.024; inter-subject gestational weight gain, p = 0.011). Newborn length was associated with leptin concentration at Week 28 (leptin effect, p = 0.003; effect of gestational weight gain, p = 0.722). Conclusions : Pregnant adolescents with leptin

  16. A regression approach for Zircaloy-2 in-reactor creep constitutive equations

    International Nuclear Information System (INIS)

    Yung Liu, Y.; Bement, A.L.

    1977-01-01

    In this paper the methodology of multiple regressions as applied to Zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor Zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) When there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets. (2) Regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections. Multiple regression analysis performed on a set of carefully selected Zircaloy-2 in-reactor creep data leads to a model which provides excellent correlations for the data. (Auth.)

  17. Association between gestational weight gain according to body mass index and postpartum weight in a large cohort of Danish women.

    Science.gov (United States)

    Rode, Line; Kjærgaard, Hanne; Ottesen, Bent; Damm, Peter; Hegaard, Hanne K

    2012-02-01

    Our aim was to investigate the association between gestational weight gain (GWG) and postpartum weight retention (PWR) in pre-pregnancy underweight, normal weight, overweight or obese women, with emphasis on the American Institute of Medicine (IOM) recommendations. We performed secondary analyses on data based on questionnaires from 1,898 women from the "Smoke-free Newborn Study" conducted 1996-1999 at Hvidovre Hospital, Denmark. Relationship between GWG and PWR was examined according to BMI as a continuous variable and in four groups. Association between PWR and GWG according to IOM recommendations was tested by linear regression analysis and the association between PWR ≥ 5 kg (11 lbs) and GWG by logistic regression analysis. Mean GWG and mean PWR were constant for all BMI units until 26-27 kg/m(2). After this cut-off mean GWG and mean PWR decreased with increasing BMI. Nearly 40% of normal weight, 60% of overweight and 50% of obese women gained more than recommended during pregnancy. For normal weight and overweight women with GWG above recommendations the OR of gaining ≥ 5 kg (11 lbs) 1-year postpartum was 2.8 (95% CI 2.0-4.0) and 2.8 (95% CI 1.3-6.2, respectively) compared to women with GWG within recommendations. GWG above IOM recommendations significantly increases normal weight, overweight and obese women's risk of retaining weight 1 year after delivery. Health personnel face a challenge in prenatal counseling as 40-60% of these women gain more weight than recommended for their BMI. As GWG is potentially modifiable, our study should be followed by intervention studies focusing on GW.

  18. Diffusion-weighted imaging of tumor recurrencies and posttherapeutical soft-tissue changes in humans

    International Nuclear Information System (INIS)

    Baur, A.; Huber, A.; Reiser, M.; Arbogast, S.; Duerr, H.R.; Zysk, S.; Wendtner, C.; Deimling, M.

    2001-01-01

    The aim of this study was to examine soft tissue tumor recurrences and posttherapeutic soft tissue changes in humans with a diffusion-weighted steady-state free precession (SSFP) sequence. Twenty-four patients with 29 pathologies of the pelvis or the extremities were examined. The lesions were classified as follows: group 1, recurrent viable tumors (n = 10); group 2, postoperative hygromas (n = 7); and group 3, posttherapeutic reactive inflammatory muscle changes (n = 12). The sequence protocol in these patients consisted of short tau inversion recovery images, T2-weighted spin-echo (SE), pre- and postcontrast T1-weighted SE images and the diffusion-weighted SSFP sequence. The signal loss on diffusion-weighting was evaluated visually on a four-grade scale and quantitatively. The signal intensities were measured in regions of interest and a regression analysis was performed. Statistical analyses was performed utilizing the Student's t-test. The signal loss was significantly higher for hygromas and edematous muscle changes than for recurrent tumors (p < 0.001) indicating higher diffusion of water protons. The regression coefficient was -0.11 (mean) for tumors. Hygromas had a significantly higher signal loss than inflammatory edematous muscle changes (p < 0.01). The regression coefficients were -0.29 (mean) for hygromas and -0.22 (mean) for edematous muscle changes. The SSFP sequence seems to be a suitable method for diffusion-weighted imaging of the musculoskeletal system in humans. These preliminary results suggest that the signal loss and the regression coefficients can be used to characterize different types of tissue. (orig.)

  19. Enhancement of Visual Field Predictions with Pointwise Exponential Regression (PER) and Pointwise Linear Regression (PLR).

    Science.gov (United States)

    Morales, Esteban; de Leon, John Mark S; Abdollahi, Niloufar; Yu, Fei; Nouri-Mahdavi, Kouros; Caprioli, Joseph

    2016-03-01

    The study was conducted to evaluate threshold smoothing algorithms to enhance prediction of the rates of visual field (VF) worsening in glaucoma. We studied 798 patients with primary open-angle glaucoma and 6 or more years of follow-up who underwent 8 or more VF examinations. Thresholds at each VF location for the first 4 years or first half of the follow-up time (whichever was greater) were smoothed with clusters defined by the nearest neighbor (NN), Garway-Heath, Glaucoma Hemifield Test (GHT), and weighting by the correlation of rates at all other VF locations. Thresholds were regressed with a pointwise exponential regression (PER) model and a pointwise linear regression (PLR) model. Smaller root mean square error (RMSE) values of the differences between the observed and the predicted thresholds at last two follow-ups indicated better model predictions. The mean (SD) follow-up times for the smoothing and prediction phase were 5.3 (1.5) and 10.5 (3.9) years. The mean RMSE values for the PER and PLR models were unsmoothed data, 6.09 and 6.55; NN, 3.40 and 3.42; Garway-Heath, 3.47 and 3.48; GHT, 3.57 and 3.74; and correlation of rates, 3.59 and 3.64. Smoothed VF data predicted better than unsmoothed data. Nearest neighbor provided the best predictions; PER also predicted consistently more accurately than PLR. Smoothing algorithms should be used when forecasting VF results with PER or PLR. The application of smoothing algorithms on VF data can improve forecasting in VF points to assist in treatment decisions.

  20. Virtual machine consolidation enhancement using hybrid regression algorithms

    Directory of Open Access Journals (Sweden)

    Amany Abdelsamea

    2017-11-01

    the ESV metric by about 24% better than other single factor regression algorithms (LR and LRR. Also we developed Hybrid Local Regression Host Overload Detection algorithm (HLRHOD that is based on local regression using hybrid factors. It outperforms the single factor algorithms.

  1. Weight preoccupation as a function of observed physical attractiveness: ethnic differences among normal-weight adolescent females.

    Science.gov (United States)

    Colabianchi, Natalie; Ievers-Landis, Carolyn E; Borawski, Elaine A

    2006-09-01

    To examine the association between observer ratings of physical attractiveness and weight preoccupation for female adolescents, and to explore any ethnic differences between Caucasian, African-American, and Hispanic females. Normal-weight female adolescents who had participated in the National Longitudinal Study of Adolescent Health in-home Wave II survey were included (n = 4,324). Physical attractiveness ratings were made in vivo by interviewers. Using logistic regression models stratified by ethnicity, the associations between observer-rated attractiveness and weight preoccupation were examined after controlling for demographics, measured body mass index (BMI) and psychosocial factors. Caucasian female adolescents perceived as being more attractive reported significantly greater weight preoccupation compared with those rated as being less attractive. Observed attractiveness did not relate to weight preoccupation among African-American or Hispanic youth when controlling for other factors. For Caucasian female adolescents, being perceived by others as more attractive may be a risk factor for disordered eating.

  2. Regression: A Bibliography.

    Science.gov (United States)

    Pedrini, D. T.; Pedrini, Bonnie C.

    Regression, another mechanism studied by Sigmund Freud, has had much research, e.g., hypnotic regression, frustration regression, schizophrenic regression, and infra-human-animal regression (often directly related to fixation). Many investigators worked with hypnotic age regression, which has a long history, going back to Russian reflexologists.…

  3. Length-weight relationship and condition factor of clarias gariepinus ...

    African Journals Online (AJOL)

    Length-Weight relationship and condition factor of Clarias gariepinus and Tilapia Zillii were studiedin lake Alau and Monguno hatchery, both in Borno State of Nigeria, for a period of two weeks. A total of 98 C. gariepinus and 140. T. zillii were measured. The length-weight regression coefficient (b) for both fishes in lake Alau ...

  4. Excess body weight in children may increase the length of hospital stay

    Directory of Open Access Journals (Sweden)

    Maria Teresa Bechere Fernandes

    2015-02-01

    Full Text Available OBJECTIVES: To investigate the prevalence of excess body weight in the pediatric ward of University Hospital and to test both the association between initial nutritional diagnosis and the length of stay and the in-hospital variation in nutritional status. METHODS: Retrospective cohort study based on information entered in clinical records from University Hospital. The data were collected from a convenience sample of 91 cases among children aged one to 10 years admitted to the hospital in 2009. The data that characterize the sample are presented in a descriptive manner. Additionally, we performed a multivariate linear regression analysis adjusted for age and gender. RESULTS: Nutritional classification at baseline showed that 87.8% of the children had a normal weight and that 8.9% had excess weight. The linear regression models showed that the average weight loss z-score of the children with excess weight compared with the group with normal weight was −0.48 (p = 0.018 and that their length of stay was 2.37 days longer on average compared with that of the normal-weight group (p = 0.047. CONCLUSIONS: The length of stay and loss of weight at the hospital may be greater among children with excess weight than among children with normal weight.

  5. Bias and Uncertainty in Regression-Calibrated Models of Groundwater Flow in Heterogeneous Media

    DEFF Research Database (Denmark)

    Cooley, R.L.; Christensen, Steen

    2006-01-01

    small. Model error is accounted for in the weighted nonlinear regression methodology developed to estimate θ* and assess model uncertainties by incorporating the second-moment matrix of the model errors into the weight matrix. Techniques developed by statisticians to analyze classical nonlinear...... are reduced in magnitude. Biases, correction factors, and confidence and prediction intervals were obtained for a test problem for which model error is large to test robustness of the methodology. Numerical results conform with the theoretical analysis....

  6. Breastfeeding reduces postpartum weight retention

    DEFF Research Database (Denmark)

    Baker, Jennifer Lyn; Gamborg, Michael; Heitmann, Berit L

    2008-01-01

    BACKGROUND: Weight gained during pregnancy and not lost postpartum may contribute to obesity in women of childbearing age. OBJECTIVE: We aimed to determine whether breastfeeding reduces postpartum weight retention (PPWR) in a population among which full breastfeeding is common and breastfeeding...... duration is long. DESIGN: We selected women from the Danish National Birth Cohort who ever breastfed (>98%), and we conducted the interviews at 6 (n = 36 030) and 18 (n = 26 846) mo postpartum. We used regression analyses to investigate whether breastfeeding (scored to account for duration and intensity......) reduced PPWR at 6 and 18 mo after adjustment for maternal prepregnancy body mass index (BMI) and gestational weight gain (GWG). RESULTS: GWG was positively (P postpartum. Breastfeeding was negatively associated with PPWR in all women but those...

  7. Advanced statistics: linear regression, part I: simple linear regression.

    Science.gov (United States)

    Marill, Keith A

    2004-01-01

    Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

  8. Effect of Workplace Weight Management on Health Care Expenditures and Quality of Life.

    Science.gov (United States)

    Michaud, Tzeyu L; Nyman, John A; Jutkowitz, Eric; Su, Dejun; Dowd, Bryan; Abraham, Jean M

    2016-11-01

    We examined the effectiveness of the weight management program used by the University of Minnesota in reducing health care expenditures and improving quality of life of its employees, and also in reducing their absenteeism during a 3-year intervention. A differences-in-differences regression approach was used to estimate the effect of weight management participation. We further applied ordinary least squares regression models with fixed effects to estimate the effect in an alternative analysis. Participation in the weight management program significantly reduced health care expenditures by $69 per month for employees, spouses, and dependents, and by $73 for employees only. Quality-of-life weights were 0.0045 points higher for participating employees than for nonparticipating ones. No significant effect was found for absenteeism. The workplace weight management used by the University of Minnesota reduced health care expenditures and improved quality of life.

  9. The relationship between weight stigma and eating behavior is explained by weight bias internalization and psychological distress.

    Science.gov (United States)

    O'Brien, Kerry S; Latner, Janet D; Puhl, Rebecca M; Vartanian, Lenny R; Giles, Claudia; Griva, Konstadina; Carter, Adrian

    2016-07-01

    Weight stigma is associated with a range of negative outcomes, including disordered eating, but the psychological mechanisms underlying these associations are not well understood. The present study tested whether the association between weight stigma experiences and disordered eating behaviors (emotional eating, uncontrolled eating, and loss-of-control eating) are mediated by weight bias internalization and psychological distress. Six-hundred and thirty-four undergraduate university students completed an online survey assessing weight stigma, weight bias internalization, psychological distress, disordered eating, along with demographic characteristics (i.e., age, gender, weight status). Statistical analyses found that weight stigma was significantly associated with all measures of disordered eating, and with weight bias internalization and psychological distress. In regression and mediation analyses accounting for age, gender and weight status, weight bias internalization and psychological distress mediated the relationship between weight stigma and disordered eating behavior. Thus, weight bias internalization and psychological distress appear to be important factors underpinning the relationship between weight stigma and disordered eating behaviors, and could be targets for interventions, such as, psychological acceptance and mindfulness therapy, which have been shown to reduce the impact of weight stigma. The evidence for the health consequences resulting from weight stigma is becoming clear. It is important that health and social policy makers are informed of this literature and encouraged develop anti-weight stigma policies for school, work, and medical settings. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Practical guidance for conducting mediation analysis with multiple mediators using inverse odds ratio weighting.

    Science.gov (United States)

    Nguyen, Quynh C; Osypuk, Theresa L; Schmidt, Nicole M; Glymour, M Maria; Tchetgen Tchetgen, Eric J

    2015-03-01

    Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting (IORW) to estimate natural direct and indirect effects for mediation analyses. IORW takes advantage of the odds ratio's invariance property and condenses information on the odds ratio for the relationship between the exposure (treatment) and multiple mediators, conditional on covariates, by regressing exposure on mediators and covariates. The inverse of the covariate-adjusted exposure-mediator odds ratio association is used to weight the primary analytical regression of the outcome on treatment. The treatment coefficient in such a weighted regression estimates the natural direct effect of treatment on the outcome, and indirect effects are identified by subtracting direct effects from total effects. Weighting renders treatment and mediators independent, thereby deactivating indirect pathways of the mediators. This new mediation technique accommodates multiple discrete or continuous mediators. IORW is easily implemented and is appropriate for any standard regression model, including quantile regression and survival analysis. An empirical example is given using data from the Moving to Opportunity (1994-2002) experiment, testing whether neighborhood context mediated the effects of a housing voucher program on obesity. Relevant Stata code (StataCorp LP, College Station, Texas) is provided. © The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  11. A comparison of Cox and logistic regression for use in genome-wide association studies of cohort and case-cohort design.

    Science.gov (United States)

    Staley, James R; Jones, Edmund; Kaptoge, Stephen; Butterworth, Adam S; Sweeting, Michael J; Wood, Angela M; Howson, Joanna M M

    2017-06-01

    Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort designs, as it is less computationally expensive. Although Cox and logistic regression models have been compared previously in cohort studies, this work does not completely cover the GWAS setting nor extend to the case-cohort study design. Here, we evaluated Cox and logistic regression applied to cohort and case-cohort genetic association studies using simulated data and genetic data from the EPIC-CVD study. In the cohort setting, there was a modest improvement in power to detect SNP-disease associations using Cox regression compared with logistic regression, which increased as the disease incidence increased. In contrast, logistic regression had more power than (Prentice weighted) Cox regression in the case-cohort setting. Logistic regression yielded inflated effect estimates (assuming the hazard ratio is the underlying measure of association) for both study designs, especially for SNPs with greater effect on disease. Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies. First to analyse all SNPs with logistic regression to identify associated variants below a pre-defined P-value threshold, and second to fit Cox regression (appropriately weighted in case-cohort studies) to those identified SNPs to ensure accurate estimation of association with disease.

  12. Use of geographically weighted logistic regression to quantify spatial variation in the environmental and sociodemographic drivers of leptospirosis in Fiji: a modelling study.

    Science.gov (United States)

    Mayfield, Helen J; Lowry, John H; Watson, Conall H; Kama, Mike; Nilles, Eric J; Lau, Colleen L

    2018-05-01

    Leptospirosis is a globally important zoonotic disease, with complex exposure pathways that depend on interactions between human beings, animals, and the environment. Major drivers of outbreaks include flooding, urbanisation, poverty, and agricultural intensification. The intensity of these drivers and their relative importance vary between geographical areas; however, non-spatial regression methods are incapable of capturing the spatial variations. This study aimed to explore the use of geographically weighted logistic regression (GWLR) to provide insights into the ecoepidemiology of human leptospirosis in Fiji. We obtained field data from a cross-sectional community survey done in 2013 in the three main islands of Fiji. A blood sample obtained from each participant (aged 1-90 years) was tested for anti-Leptospira antibodies and household locations were recorded using GPS receivers. We used GWLR to quantify the spatial variation in the relative importance of five environmental and sociodemographic covariates (cattle density, distance to river, poverty rate, residential setting [urban or rural], and maximum rainfall in the wettest month) on leptospirosis transmission in Fiji. We developed two models, one using GWLR and one with standard logistic regression; for each model, the dependent variable was the presence or absence of anti-Leptospira antibodies. GWLR results were compared with results obtained with standard logistic regression, and used to produce a predictive risk map and maps showing the spatial variation in odds ratios (OR) for each covariate. The dataset contained location information for 2046 participants from 1922 households representing 81 communities. The Aikaike information criterion value of the GWLR model was 1935·2 compared with 1254·2 for the standard logistic regression model, indicating that the GWLR model was more efficient. Both models produced similar OR for the covariates, but GWLR also detected spatial variation in the effect of each

  13. Alcohol Use, Eating Patterns, and Weight Behaviors in a University Population

    Science.gov (United States)

    Nelson, Melissa C.; Lust, Katherine; Story, Mary; Ehlinger, Ed

    2009-01-01

    Objective: To explore associations between alcohol, alcohol-related eating, and weight-related health indicators. Methods: Cross-sectional, multivariate regression of weight behaviors, binge drinking, and alcohol-related eating, using self-reported student survey data (n = 3206 undergraduates/graduates). Results: Binge drinking was associated with…

  14. Consistency of the least weighted squares under heteroscedasticity

    Czech Academy of Sciences Publication Activity Database

    Víšek, Jan Ámos

    2011-01-01

    Roč. 2011, č. 47 (2011), s. 179-206 ISSN 0023-5954 Grant - others:GA UK(CZ) GA402/09/055 Institutional research plan: CEZ:AV0Z10750506 Keywords : Regression * Consistency * The least weighted squares * Heteroscedasticity Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.454, year: 2011 http://library.utia.cas.cz/separaty/2011/SI/visek-consistency of the least weighted squares under heteroscedasticity.pdf

  15. Peer influence on pre-adolescent girls' snack intake: effects of weight status.

    Science.gov (United States)

    Salvy, Sarah-Jeanne; Romero, Natalie; Paluch, Rocco; Epstein, Leonard H

    2007-07-01

    Although most eating occurs in a social context, the effects of peer influence on child eating have not been the object of systematic experimental study. The present study assesses the effects of peer influence on lean and overweight pre-adolescent girls' snack intake as a function of the co-eaters' weight status. The weight status of the participants was varied by studying weight discordant dyads (i.e., one lean and one overweight participant) and weight concordant dyads (i.e., both members of the dyads were either lean or overweight). Results from the random regression model indicate that overweight girls eating with an overweight peer consumed more kilocalories than overweight participants eating with a normal-weight peer. Normal-weight participants eating with overweight peers ate similar amounts as those eating with lean eating companions. The regression model improved when the partners' food intake was entered in the model, indicating that the peers' intake was a significant predictor of participants' snack consumption. This study underscores differences in responses to the social environment between overweight and non-overweight youths.

  16. Task demands affect spatial reference frame weighting during tactile localization in sighted and congenitally blind adults.

    Directory of Open Access Journals (Sweden)

    Jonathan T W Schubert

    Full Text Available Task demands modulate tactile localization in sighted humans, presumably through weight adjustments in the spatial integration of anatomical, skin-based, and external, posture-based information. In contrast, previous studies have suggested that congenitally blind humans, by default, refrain from automatic spatial integration and localize touch using only skin-based information. Here, sighted and congenitally blind participants localized tactile targets on the palm or back of one hand, while ignoring simultaneous tactile distractors at congruent or incongruent locations on the other hand. We probed the interplay of anatomical and external location codes for spatial congruency effects by varying hand posture: the palms either both faced down, or one faced down and one up. In the latter posture, externally congruent target and distractor locations were anatomically incongruent and vice versa. Target locations had to be reported either anatomically ("palm" or "back" of the hand, or externally ("up" or "down" in space. Under anatomical instructions, performance was more accurate for anatomically congruent than incongruent target-distractor pairs. In contrast, under external instructions, performance was more accurate for externally congruent than incongruent pairs. These modulations were evident in sighted and blind individuals. Notably, distractor effects were overall far smaller in blind than in sighted participants, despite comparable target-distractor identification performance. Thus, the absence of developmental vision seems to be associated with an increased ability to focus tactile attention towards a non-spatially defined target. Nevertheless, that blind individuals exhibited effects of hand posture and task instructions in their congruency effects suggests that, like the sighted, they automatically integrate anatomical and external information during tactile localization. Moreover, spatial integration in tactile processing is, thus, flexibly

  17. Task demands affect spatial reference frame weighting during tactile localization in sighted and congenitally blind adults.

    Science.gov (United States)

    Schubert, Jonathan T W; Badde, Stephanie; Röder, Brigitte; Heed, Tobias

    2017-01-01

    Task demands modulate tactile localization in sighted humans, presumably through weight adjustments in the spatial integration of anatomical, skin-based, and external, posture-based information. In contrast, previous studies have suggested that congenitally blind humans, by default, refrain from automatic spatial integration and localize touch using only skin-based information. Here, sighted and congenitally blind participants localized tactile targets on the palm or back of one hand, while ignoring simultaneous tactile distractors at congruent or incongruent locations on the other hand. We probed the interplay of anatomical and external location codes for spatial congruency effects by varying hand posture: the palms either both faced down, or one faced down and one up. In the latter posture, externally congruent target and distractor locations were anatomically incongruent and vice versa. Target locations had to be reported either anatomically ("palm" or "back" of the hand), or externally ("up" or "down" in space). Under anatomical instructions, performance was more accurate for anatomically congruent than incongruent target-distractor pairs. In contrast, under external instructions, performance was more accurate for externally congruent than incongruent pairs. These modulations were evident in sighted and blind individuals. Notably, distractor effects were overall far smaller in blind than in sighted participants, despite comparable target-distractor identification performance. Thus, the absence of developmental vision seems to be associated with an increased ability to focus tactile attention towards a non-spatially defined target. Nevertheless, that blind individuals exhibited effects of hand posture and task instructions in their congruency effects suggests that, like the sighted, they automatically integrate anatomical and external information during tactile localization. Moreover, spatial integration in tactile processing is, thus, flexibly adapted by top

  18. Weight Status and High Blood Pressure Among Low-Income African American Men

    Science.gov (United States)

    Bruce, Marino A.; Beech, Bettina M.; Edwards, Christopher L.; Sims, Mario; Scarinci, Isabel; Whitfield, Keith E.; Gilbert, Keon; Crook, Errol D.

    2016-01-01

    Obesity is a biological risk factor or comorbidity that has not received much attention from scientists studying hypertension among African American men. The purpose of this study was to examine the relationship between weight status and high blood pressure among African American men with few economic resources. The authors used surveillance data collected from low-income adults attending community- and faith-based primary care clinics in West Tennessee to estimate pooled and group-specific regression models of high blood pressure. The results from group-specific logistic regression models indicate that the factors associated with hypertension varied considerably by weight status. This study provides a glimpse into the complex relationship between weight status and high blood pressure status among African American men. Additional research is needed to identify mechanisms through which excess weight affects the development and progression of high blood pressure. PMID:20937738

  19. Decoding suprathreshold stochastic resonance with optimal weights

    International Nuclear Information System (INIS)

    Xu, Liyan; Vladusich, Tony; Duan, Fabing; Gunn, Lachlan J.; Abbott, Derek; McDonnell, Mark D.

    2015-01-01

    We investigate an array of stochastic quantizers for converting an analog input signal into a discrete output in the context of suprathreshold stochastic resonance. A new optimal weighted decoding is considered for different threshold level distributions. We show that for particular noise levels and choices of the threshold levels optimally weighting the quantizer responses provides a reduced mean square error in comparison with the original unweighted array. However, there are also many parameter regions where the original array provides near optimal performance, and when this occurs, it offers a much simpler approach than optimally weighting each quantizer's response. - Highlights: • A weighted summing array of independently noisy binary comparators is investigated. • We present an optimal linearly weighted decoding scheme for combining the comparator responses. • We solve for the optimal weights by applying least squares regression to simulated data. • We find that the MSE distortion of weighting before summation is superior to unweighted summation of comparator responses. • For some parameter regions, the decrease in MSE distortion due to weighting is negligible

  20. Application of Logistic Regression Tree Model in Determining Habitat Distribution of Astragalus verus

    Directory of Open Access Journals (Sweden)

    M. Saki

    2013-03-01

    Full Text Available The relationship between plant species and environmental factors has always been a central issue in plant ecology. With rising power of statistical techniques, geo-statistics and geographic information systems (GIS, the development of predictive habitat distribution models of organisms has rapidly increased in ecology. This study aimed to evaluate the ability of Logistic Regression Tree model to create potential habitat map of Astragalus verus. This species produces Tragacanth and has economic value. A stratified- random sampling was applied to 100 sites (50 presence- 50 absence of given species, and produced environmental and edaphic factors maps by using Kriging and Inverse Distance Weighting methods in the ArcGIS software for the whole study area. Relationships between species occurrence and environmental factors were determined by Logistic Regression Tree model and extended to the whole study area. The results indicated species occurrence has strong correlation with environmental factors such as mean daily temperature and clay, EC and organic carbon content of the soil. Species occurrence showed direct relationship with mean daily temperature and clay and organic carbon, and inverse relationship with EC. Model accuracy was evaluated both by Cohen’s kappa statistics (κ and by area under Receiver Operating Characteristics curve based on independent test data set. Their values (kappa=0.9, Auc of ROC=0.96 indicated the high power of LRT to create potential habitat map on local scales. This model, therefore, can be applied to recognize potential sites for rangeland reclamation projects.

  1. Spontaneous regression of a large hepatocellular carcinoma: case report

    Directory of Open Access Journals (Sweden)

    Alqutub, Adel

    2011-01-01

    Full Text Available The prognosis of untreated advanced hepatocellular carcinoma (HCC is grim with a median survival of less than 6 months. Spontaneous regression of HCC has been defined as the disappearance of the hepatic lesions in the absence of any specific therapy. The spontaneous regression of a very large HCC is very rare and limited data is available in the English literature. We describe spontaneous regression of hepatocellular carcinoma in a 65-year-old male who presented to our clinic with vague abdominal pain and weight loss of two months duration. He was found to have multiple hepatic lesions with elevation of serum alpha-fetoprotein (AFP level to 6,500 µg/L (normal <20 µg/L. Computed tomography revealed advanced HCC replacing almost 80% of the right hepatic lobe. Without any intervention the patient showed gradual improvement over a period of few months. Follow-up CT scan revealed disappearance of hepatic lesions with progressive decline of AFP levels to normal. Various mechanisms have been postulated to explain this rare phenomenon, but the exact mechanism remains a mystery.

  2. The effects of maternal weight gain patterns on term birth weight in African-American women

    Science.gov (United States)

    Misra, Vinod K.; Hobel, Calvin J.; Sing, Charles F.

    2010-01-01

    Objective The goals of our study were 1) to estimate the trends in maternal weight gain patterns and 2) to estimate the influence of variation in maternal weight and rate of weight gain over different time periods in gestation on variation in birth weight in African-American and non-African-American gravidas. Study Design and Setting Data from a prospective cohort study in which pregnant women were monitored at multiple time points during pregnancy were analyzed. Maternal weight was measured at three times during pregnancy, preconception (W0); 16-20 weeks gestation (W1); and 30-36 weeks gestation (W2), in a cohort of 435 women with full-term singleton pregnancies. The relationship between gestational age-adjusted birth weight (aBW) and measures of maternal weight and rate of weight gain across pregnancy was estimated using a multivariable longitudinal regression analysis stratified on African-American race. Results The aBW was significantly associated with maternal weight measured at any visit in both strata. For African-American women, variation in aBW was significantly associated with variation in the rate of maternal weight gain in the first half of pregnancy (W01) but not the rate of maternal weight gain in the second half of pregnancy (W12); while for non-African-American women, variation in aBW was significantly associated with W12 but not W01. Conclusion Factors influencing the relationship between aBW and maternal weight gain patterns depend on the context of the pregnancy defined by race. Clinical decisions and recommendations about maternal weight and weight gain during pregnancy may need to account for such heterogeneity. PMID:20632908

  3. Financial Time Series Forecasting Using Directed-Weighted Chunking SVMs

    Directory of Open Access Journals (Sweden)

    Yongming Cai

    2014-01-01

    Full Text Available Support vector machines (SVMs are a promising alternative to traditional regression estimation approaches. But, when dealing with massive-scale data set, there exist many problems, such as the long training time and excessive demand of memory space. So, the SVMs algorithm is not suitable to deal with financial time series data. In order to solve these problems, directed-weighted chunking SVMs algorithm is proposed. In this algorithm, the whole training data set is split into several chunks, and then the support vectors are obtained on each subset. Furthermore, the weighted support vector regressions are calculated to obtain the forecast model on the new working data set. Our directed-weighted chunking algorithm provides a new method of support vectors decomposing and combining according to the importance of chunks, which can improve the operation speed without reducing prediction accuracy. Finally, IBM stock daily close prices data are used to verify the validity of the proposed algorithm.

  4. Diffusion-weighted magnetic resonance imaging: a potential non-invasive marker of tumour aggressiveness in localized prostate cancer

    International Nuclear Information System (INIS)

    Souza, N.M. de; Riches, S.F.; Van As, N.J.; Morgan, V.A.; Ashley, S.A.; Fisher, C.; Payne, G.S.; Parker, C.

    2008-01-01

    Aim: To evaluate diffusion-weighted magnetic resonance imaging (DW-MRI) as a marker for disease aggressiveness by comparing tumour apparent diffusion coefficients (ADCs) between patients with low- versus higher-risk localized prostate cancer. Method: Forty-four consecutive patients classified as low- [n = 26, stageT1/T2a, Gleason score ≤ 6, prostate-specific antigen (PSA) 10 (group 2)] risk, who subsequently were monitored with active surveillance or started neoadjuvant hormone and radiotherapy, respectively, underwent endorectal MRI. T2-weighted (T2W) and DW images (5 b values, 0-800 s/mm 2 ) were acquired and isotropic ADC maps generated. Regions of interest (ROIs) on T2W axial images [around whole prostate, central gland (CG), and tumour] were transferred to ADC maps. Tumour, CG, and peripheral zone (PZ = whole prostate minus CG and tumour) ADCs (fast component from b = 0-100 s/mm 2 , slow component from b = 100-800 s/mm 2 ) were compared. Results: T2W-defined tumour volume medians, and quartiles were 1.2 cm 3 , 0.7 and 3.3 cm 3 (group 1); and 6 cm 3 , 1.3 and 16.5 cm 3 (group 2). There were significant differences in both ADC fast (1778 ± 264 x 10 -6 versus 1583 ± 283 x 10 -6 mm 2 /s, p = 0.03) and ADC slow (1379 ± 321 x 10 -6 versus 1196 ± 158 x 10 -6 mm 2 /s, p = 0.001) between groups. Tumour volume (p = 0.002) and ADC slow (p = 0.005) were significant differentiators of risk group. Conclusion: Significant differences in tumour ADCs exist between patients with low-risk, and those with higher-risk localized prostate cancer. DW-MRI merits further study with respect to clinical outcomes

  5. The Prediction Properties of Inverse and Reverse Regression for the Simple Linear Calibration Problem

    Science.gov (United States)

    Parker, Peter A.; Geoffrey, Vining G.; Wilson, Sara R.; Szarka, John L., III; Johnson, Nels G.

    2010-01-01

    The calibration of measurement systems is a fundamental but under-studied problem within industrial statistics. The origins of this problem go back to basic chemical analysis based on NIST standards. In today's world these issues extend to mechanical, electrical, and materials engineering. Often, these new scenarios do not provide "gold standards" such as the standard weights provided by NIST. This paper considers the classic "forward regression followed by inverse regression" approach. In this approach the initial experiment treats the "standards" as the regressor and the observed values as the response to calibrate the instrument. The analyst then must invert the resulting regression model in order to use the instrument to make actual measurements in practice. This paper compares this classical approach to "reverse regression," which treats the standards as the response and the observed measurements as the regressor in the calibration experiment. Such an approach is intuitively appealing because it avoids the need for the inverse regression. However, it also violates some of the basic regression assumptions.

  6. The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

    Science.gov (United States)

    Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

    2014-09-01

    In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

  7. Identifying areas at risk of low birth weight using spatial epidemiology: A small area surveillance study.

    Science.gov (United States)

    Insaf, Tabassum Z; Talbot, Thomas

    2016-07-01

    To assess the geographic distribution of Low Birth Weight (LBW) in New York State among singleton births using a spatial regression approach in order to identify priority areas for public health actions. LBW was defined as birth weight less than 2500g. Geocoded data from 562,586 birth certificates in New York State (years 2008-2012) were merged with 2010 census data at the tract level. To provide stable estimates and maintain confidentiality, data were aggregated to yield 1268 areas of analysis. LBW prevalence among singleton births was related with area-level behavioral, socioeconomic and demographic characteristics using a Poisson mixed effects spatial error regression model. Observed low birth weight showed statistically significant auto-correlation in our study area (Moran's I 0.16 p value 0.0005). After over-dispersion correction and accounting for fixed effects for selected social determinants, spatial autocorrelation was fully accounted for (Moran's I-0.007 p value 0.241). The proportion of LBW was higher in areas with larger Hispanic or Black populations and high smoking prevalence. Smoothed maps with predicted prevalence were developed to identify areas at high risk of LBW. Spatial patterns of residual variation were analyzed to identify unique risk factors. Neighborhood racial composition contributes to disparities in LBW prevalence beyond differences in behavioral and socioeconomic factors. Small-area analyses of LBW can identify areas for targeted interventions and display unique local patterns that should be accounted for in prevention strategies. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  8. Factors associated with low birth weight in Goiás State

    Directory of Open Access Journals (Sweden)

    Ruth Minamisava

    2006-12-01

    Full Text Available ABSTRACT: Low birth weight (LBW is public health problem, because they are associated with increased risk of morbidity and mortality. No previous studies on factors associated with LBW carried out in central Brazil were found in the literature. The main aims of this study were to determine the prevalence and the factors associated with LBW in children born alive in the State of Goiás, Brazil. A cross-sectional analysis was performed using data from the Live Births Information System from the Brazilian Health Ministry. All 92.745 singleton births in the State of Goiás during the year of 2000 were examined. Logistic regression analysis was used to examine the factors associated with LBW (< 2500 g. In Goiás, the prevalence of LBW was 5.96% and the most important factors associated with LBW were: prematurity, young and older mothers, unmarried women, mother illiteracy, mothers who had less than seven prenatal care visits, non-hospital delivery, and female infants. Local public health actions are necessary to reduce inequalities in infant and maternal care. KEYWORDS: newborn, birth weight, prenatal care.

  9. Using Incomplete Information for Complete Weight Annotation of Road Networks

    DEFF Research Database (Denmark)

    Yang, Bin; Kaul, Manohar; Jensen, Christian Søndergaard

    2014-01-01

    to solve the problem. Specifically, the problem is modeled as a regression problem and solved by minimizing a judiciously designed objective function that takes into account the topology of the road network. In particular, the use of weighted PageRank values of edges is explored for assigning appropriate...... weights to all edges, and the property of directional adjacency of edges is also taken into account to assign weights. Empirical studies with weights capturing travel time and GHG emissions on two road networks (Skagen, Denmark, and North Jutland, Denmark) offer insight into the design properties...

  10. Ordinary least square regression, orthogonal regression, geometric mean regression and their applications in aerosol science

    International Nuclear Information System (INIS)

    Leng Ling; Zhang Tianyi; Kleinman, Lawrence; Zhu Wei

    2007-01-01

    Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. Errors often exist for both measurements. In this work, we examine two regression approaches available to accommodate this situation. They are orthogonal regression and geometric mean regression. Comparisons are made theoretically as well as numerically through an aerosol study examining whether the ratio of organic aerosol to CO would change with age

  11. Translating Response During Therapy into Ultimate Treatment Outcome: A Personalized 4-Dimensional MRI Tumor Volumetric Regression Approach in Cervical Cancer

    International Nuclear Information System (INIS)

    Mayr, Nina A.; Wang, Jian Z.; Lo, Simon S.; Zhang Dongqing; Grecula, John C.; Lu Lanchun; Montebello, Joseph F.; Fowler, Jeffrey M.; Yuh, William T.C.

    2010-01-01

    Purpose: To assess individual volumetric tumor regression pattern in cervical cancer during therapy using serial four-dimensional MRI and to define the regression parameters' prognostic value validated with local control and survival correlation. Methods and Materials: One hundred and fifteen patients with Stage IB 2 -IVA cervical cancer treated with radiation therapy (RT) underwent serial MRI before (MRI 1) and during RT, at 2-2.5 weeks (MRI 2, at 20-25 Gy), and at 4-5 weeks (MRI 3, at 40-50 Gy). Eighty patients had a fourth MRI 1-2 months post-RT. Mean follow-up was 5.3 years. Tumor volume was measured by MRI-based three-dimensional volumetry, and plotted as dose(time)/volume regression curves. Volume regression parameters were correlated with local control, disease-specific, and overall survival. Results: Residual tumor volume, slope, and area under the regression curve correlated significantly with local control and survival. Residual volumes ≥20% at 40-50 Gy were independently associated with inferior 5-year local control (53% vs. 97%, p <0.001) and disease-specific survival rates (50% vs. 72%, p = 0.009) than smaller volumes. Patients with post-RT residual volumes ≥10% had 0% local control and 17% disease-specific survival, compared with 91% and 72% for <10% volume (p <0.001). Conclusion: Using more accurate four-dimensional volumetric regression analysis, tumor response can now be directly translated into individual patients' outcome for clinical application. Our results define two temporal thresholds critically influencing local control and survival. In patients with ≥20% residual volume at 40-50 Gy and ≥10% post-RT, the risk for local failure and death are so high that aggressive intervention may be warranted.

  12. Multiclass Prediction with Partial Least Square Regression for Gene Expression Data: Applications in Breast Cancer Intrinsic Taxonomy

    Directory of Open Access Journals (Sweden)

    Chi-Cheng Huang

    2013-01-01

    Full Text Available Multiclass prediction remains an obstacle for high-throughput data analysis such as microarray gene expression profiles. Despite recent advancements in machine learning and bioinformatics, most classification tools were limited to the applications of binary responses. Our aim was to apply partial least square (PLS regression for breast cancer intrinsic taxonomy, of which five distinct molecular subtypes were identified. The PAM50 signature genes were used as predictive variables in PLS analysis, and the latent gene component scores were used in binary logistic regression for each molecular subtype. The 139 prototypical arrays for PAM50 development were used as training dataset, and three independent microarray studies with Han Chinese origin were used for independent validation (n=535. The agreement between PAM50 centroid-based single sample prediction (SSP and PLS-regression was excellent (weighted Kappa: 0.988 within the training samples, but deteriorated substantially in independent samples, which could attribute to much more unclassified samples by PLS-regression. If these unclassified samples were removed, the agreement between PAM50 SSP and PLS-regression improved enormously (weighted Kappa: 0.829 as opposed to 0.541 when unclassified samples were analyzed. Our study ascertained the feasibility of PLS-regression in multi-class prediction, and distinct clinical presentations and prognostic discrepancies were observed across breast cancer molecular subtypes.

  13. The molecular characterization of weighted Hardy spaces

    Institute of Scientific and Technical Information of China (English)

    LI; Xingmin

    2001-01-01

    Surveys, 1993, 7: 305.[16]Shephand, N., Statistical aspects of ARCH and stochastic volatility, in Time Series Models in Econometrics, Finance and Other Fields (eds. Cox, D. R., Hinkley, D. V., Barndorff-Nielsen, O. E.), London: Chapman & Hall, 1996, 1.[17]Pantula, S. G., Estimation of autoregressive models with ARCH errors, Sankhya, Ser. B, 1988, 50: 119.[18]Campbell, J. Y., Lo, A. W., Mackinlay, A. C., The Econometrics of Financial Markets, Princeton: Princeton University Press, 1997, 488.[19]Fan, J., Gijbels, I., Local Polynomial Modeling and Its Applications, London: Chapman & Hall, 1996.[20]Lu, Z. D., A note on geometric ergodicity of autoregressive conditional heteroscedasticity (ARCH) model, Statistics and Probability Letters, 1996, 30: 305.[21]Robinson, P. M., Nonparametric estimators for time series, Journal of Time Series Analysis, 1983, 4: 185.[22]Stone, C. J., Optimal rates of convergence for nonparametric estimators, Annals of Statistics, 1980, 8: 1348.[23]Stone, C. J., Optimal global rates of convergence for nonparametric kernel regression, Annals of Statistics, 1982, 10: 1040.[24]Truong, Y. M., Stone, C. J., Nonparametric function estimation involving time series, Annals of Statistics, 1992, 20: 77.[25]Masry, E., Multivariate polynomial regression for time series; uniform strong consistency and rates, Journal of Time Series Analysis, 1996, 17: 571.[26]Ruppert, D., Wand, M. P., Multivariate locally weighted least squares regression, Annals of Statistics, 1994, 22: 1346.[27]Bollerslev, T., Generalized autoregressive conditional heteroscedasticity, Journal of Econometrics, 1986, 31: 307.[28]Engle, R. F., Granger, C. W. J., Co-integration and error-correction: representation, estimation and testing, Econometrica, 1987, 55: 251.

  14. Polynomial regression analysis and significance test of the regression function

    International Nuclear Information System (INIS)

    Gao Zhengming; Zhao Juan; He Shengping

    2012-01-01

    In order to analyze the decay heating power of a certain radioactive isotope per kilogram with polynomial regression method, the paper firstly demonstrated the broad usage of polynomial function and deduced its parameters with ordinary least squares estimate. Then significance test method of polynomial regression function is derived considering the similarity between the polynomial regression model and the multivariable linear regression model. Finally, polynomial regression analysis and significance test of the polynomial function are done to the decay heating power of the iso tope per kilogram in accord with the authors' real work. (authors)

  15. Graph-Based Semi-Supervised Learning for Indoor Localization Using Crowdsourced Data

    Directory of Open Access Journals (Sweden)

    Liye Zhang

    2017-04-01

    Full Text Available Indoor positioning based on the received signal strength (RSS of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, lots of different devices are used in crowdsourcing system and less RSS values are collected by each device. Therefore, the crowdsourced RSS values are more erroneous and can result in significant localization errors. In order to eliminate the signal strength variations across diverse devices, the Linear Regression (LR algorithm is proposed to solve the device diversity problem in crowdsourcing system. After obtaining the uniform RSS values, a graph-based semi-supervised learning (G-SSL method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. As a result, the negative effect of the erroneous measurements could be mitigated. Since the AP locations need to be known in G-SSL algorithm, the Compressed Sensing (CS method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy.

  16. Prediction model of critical weight loss in cancer patients during particle therapy.

    Science.gov (United States)

    Zhang, Zhihong; Zhu, Yu; Zhang, Lijuan; Wang, Ziying; Wan, Hongwei

    2018-01-01

    The objective of this study is to investigate the predictors of critical weight loss in cancer patients receiving particle therapy, and build a prediction model based on its predictive factors. Patients receiving particle therapy were enroled between June 2015 and June 2016. Body weight was measured at the start and end of particle therapy. Association between critical weight loss (defined as >5%) during particle therapy and patients' demographic, clinical characteristic, pre-therapeutic nutrition risk screening (NRS 2002) and BMI were evaluated by logistic regression and decision tree analysis. Finally, 375 cancer patients receiving particle therapy were included. Mean weight loss was 0.55 kg, and 11.5% of patients experienced critical weight loss during particle therapy. The main predictors of critical weight loss during particle therapy were head and neck tumour location, total radiation dose ≥70 Gy on the primary tumour, and without post-surgery, as indicated by both logistic regression and decision tree analysis. Prediction model that includes tumour locations, total radiation dose and post-surgery had a good predictive ability, with the area under receiver operating characteristic curve 0.79 (95% CI: 0.71-0.88) and 0.78 (95% CI: 0.69-0.86) for decision tree and logistic regression model, respectively. Cancer patients with head and neck tumour location, total radiation dose ≥70 Gy and without post-surgery were at higher risk of critical weight loss during particle therapy, and early intensive nutrition counselling or intervention should be target at this population. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  17. Visual grading characteristics and ordinal regression analysis during optimisation of CT head examinations.

    Science.gov (United States)

    Zarb, Francis; McEntee, Mark F; Rainford, Louise

    2015-06-01

    To evaluate visual grading characteristics (VGC) and ordinal regression analysis during head CT optimisation as a potential alternative to visual grading assessment (VGA), traditionally employed to score anatomical visualisation. Patient images (n = 66) were obtained using current and optimised imaging protocols from two CT suites: a 16-slice scanner at the national Maltese centre for trauma and a 64-slice scanner in a private centre. Local resident radiologists (n = 6) performed VGA followed by VGC and ordinal regression analysis. VGC alone indicated that optimised protocols had similar image quality as current protocols. Ordinal logistic regression analysis provided an in-depth evaluation, criterion by criterion allowing the selective implementation of the protocols. The local radiology review panel supported the implementation of optimised protocols for brain CT examinations (including trauma) in one centre, achieving radiation dose reductions ranging from 24 % to 36 %. In the second centre a 29 % reduction in radiation dose was achieved for follow-up cases. The combined use of VGC and ordinal logistic regression analysis led to clinical decisions being taken on the implementation of the optimised protocols. This improved method of image quality analysis provided the evidence to support imaging protocol optimisation, resulting in significant radiation dose savings. • There is need for scientifically based image quality evaluation during CT optimisation. • VGC and ordinal regression analysis in combination led to better informed clinical decisions. • VGC and ordinal regression analysis led to dose reductions without compromising diagnostic efficacy.

  18. Estimation Of Body Weight From Linear Body Measurements In Two ...

    African Journals Online (AJOL)

    The prediction of body weight from body girth, keel length and thigh length was studied using one hundred Ross and one hundred Anak Titan broilers. Data were collected on the birds from day-old to 9 weeks of age. Body measurement was regressed against body weight at 9 weeks of age using simple linear and ...

  19. Reduced Rank Regression

    DEFF Research Database (Denmark)

    Johansen, Søren

    2008-01-01

    The reduced rank regression model is a multivariate regression model with a coefficient matrix with reduced rank. The reduced rank regression algorithm is an estimation procedure, which estimates the reduced rank regression model. It is related to canonical correlations and involves calculating...

  20. Gender orientation and alcohol-related weight control behavior among male and female college students.

    Science.gov (United States)

    Peralta, Robert L; Barr, Peter B

    2017-01-01

    We examine weight control behavior used to (a) compensate for caloric content of heavy alcohol use; and (b) enhance the psychoactive effects of alcohol among college students. We evaluate the role of gender orientation and sex. Participants completed an online survey (N = 651; 59.9% women; 40.1% men). Weight control behavior was assessed via the Compensatory-Eating-and-Behaviors-in Response-to-Alcohol-Consumption-Scale. Control variables included sex, race/ethnicity, age, and depressive symptoms. Gender orientation was measured by the Bem Sex Role Inventory. The prevalence and probability of alcohol-related weight control behavior using ordinal logistic regression are reported. Men and women do not significantly differ in compensatory-weight-control-behavior. However, regression models suggest that recent binge drinking, other substance use, and masculine orientation are positively associated with alcohol-related weight control behavior. Sex was not a robust predictor of weight control behavior. Masculine orientation should be considered a possible risk factor for these behaviors and considered when designing prevention and intervention strategies.

  1. Agreement Between Actual and Perceived Body Weight in Adolescents and Their Weight Control Behaviors

    Directory of Open Access Journals (Sweden)

    Sun Mi Shin

    2017-06-01

    Full Text Available Background : To investigate the agreements between actual and perceived body weight status among adolescents and to identify the associations of disagreements with their weight control behaviors. Methods : This study used the secondary data of a sample survey (n=13,871 of the Seoul Student Health Examination among middle and high schools in 2010. Agreements between actual (underweight, normal, overweight, and obese, according to 2007 Korean National Growth Charts and perceived body weight status (underweight, normal, overweight, and obese were examined using Chi-square and Cohen’s kappa agreement, and then multinomial logistic regression including gender, grade, and attempt of weight control or method of weight control was done. Results : Agreements between actual and perceived body weight status were only 45.2%, and disagreements were up to 54.8%, including mild over- (20.4%, severe over- (1.8%, mild under- (29.5%, and severe under-estimation (3.1%. The kappa coefficient of agreement was only 0.19. The odds ratios on severe over-estimated perception were 1.59 (95% CI, 1.22-2.07 in female subjects, 1.78 (95% CI, 1.36-2.34 in diet control behaviors, and 1.53 (95% CI, 1.18-2.00 in exercise. The odds ratios on severe under-estimated perception were only 0.40 (95% CI, 0.32–0.50 in female subjects but 5.77 (95% CI, 3.68-9.06 in taking medication. Conclusion : There were associations of body weight control behaviors with disagreements of actual and perceived weight status. Therefore, further study is needed to identify the weight disagreement-related factors and to promote the desired weight control behaviors for adolescents.

  2. Weight-related concerns and weight-control behaviors among overweight adolescents in Delhi, India: A cross-sectional study

    Directory of Open Access Journals (Sweden)

    Shrivastav Radhika

    2011-02-01

    Full Text Available Abstract Background Obesity is emerging as a public health problem among adolescents in India. The aim of this study was to describe specific weight-related concerns among school-going youth in Delhi, India and to assess the prevalence of weight control behaviors, including healthy and unhealthy ones. Differences by weight status, gender, grade level, and school-type (a proxy for SES in this setting are considered. Methods This study is cross-sectional by design. A sample of eighth and tenth graders (n = 1818 enrolled in Private (middle-high SES and Government (low SES schools (n = 8 in Delhi, India participated. All students' height and weight were measured. Students participated in a survey of weight-related concerns and weight-control behaviors, as well. Mixed-effects regression models were used to test for differences in weight-related concerns and weight-control behaviors across key factors of interest (i.e., weight status, gender, grade level, and SES. Results The combined prevalence of obesity and overweight was 16.6%, overall. Controlling one's weight was important to overweight and non-overweight youth, alike (94.2% v. 84.8%, p p Conclusions Interventions to promote healthy weight control should be pertinent to and well-received by school-going youth in India. Healthy weight control practices need to be explicitly encouraged and unhealthy practices reduced. Future interventions should address issues specific to body image, too, as body dissatisfaction was not uncommon among youth.

  3. Animal models of maternal high fat diet exposure and effects on metabolism in offspring: a meta-regression analysis.

    Science.gov (United States)

    Ribaroff, G A; Wastnedge, E; Drake, A J; Sharpe, R M; Chambers, T J G

    2017-06-01

    Animal models of maternal high fat diet (HFD) demonstrate perturbed offspring metabolism although the effects differ markedly between models. We assessed studies investigating metabolic parameters in the offspring of HFD fed mothers to identify factors explaining these inter-study differences. A total of 171 papers were identified, which provided data from 6047 offspring. Data were extracted regarding body weight, adiposity, glucose homeostasis and lipidaemia. Information regarding the macronutrient content of diet, species, time point of exposure and gestational weight gain were collected and utilized in meta-regression models to explore predictive factors. Publication bias was assessed using Egger's regression test. Maternal HFD exposure did not affect offspring birthweight but increased weaning weight, final bodyweight, adiposity, triglyceridaemia, cholesterolaemia and insulinaemia in both female and male offspring. Hyperglycaemia was found in female offspring only. Meta-regression analysis identified lactational HFD exposure as a key moderator. The fat content of the diet did not correlate with any outcomes. There was evidence of significant publication bias for all outcomes except birthweight. Maternal HFD exposure was associated with perturbed metabolism in offspring but between studies was not accounted for by dietary constituents, species, strain or maternal gestational weight gain. Specific weaknesses in experimental design predispose many of the results to bias. © 2017 The Authors. Obesity Reviews published by John Wiley & Sons Ltd on behalf of World Obesity Federation.

  4. Weight loss efficacy of a novel mobile Diabetes Prevention Program delivery platform with human coaching

    Science.gov (United States)

    Michaelides, Andreas; Raby, Christine; Wood, Meghan; Farr, Kit

    2016-01-01

    Objective To evaluate the weight loss efficacy of a novel mobile platform delivering the Diabetes Prevention Program. Research Design and Methods 43 overweight or obese adult participants with a diagnosis of prediabetes signed-up to receive a 24-week virtual Diabetes Prevention Program with human coaching, through a mobile platform. Weight loss and engagement were the main outcomes, evaluated by repeated measures analysis of variance, backward regression, and mediation regression. Results Weight loss at 16 and 24 weeks was significant, with 56% of starters and 64% of completers losing over 5% body weight. Mean weight loss at 24 weeks was 6.58% in starters and 7.5% in completers. Participants were highly engaged, with 84% of the sample completing 9 lessons or more. In-app actions related to self-monitoring significantly predicted weight loss. Conclusions Our findings support the effectiveness of a uniquely mobile prediabetes intervention, producing weight loss comparable to studies with high engagement, with potential for scalable population health management. PMID:27651911

  5. A Weighted Least Squares Approach To Robustify Least Squares Estimates.

    Science.gov (United States)

    Lin, Chowhong; Davenport, Ernest C., Jr.

    This study developed a robust linear regression technique based on the idea of weighted least squares. In this technique, a subsample of the full data of interest is drawn, based on a measure of distance, and an initial set of regression coefficients is calculated. The rest of the data points are then taken into the subsample, one after another,…

  6. Seniors' body weight dissatisfaction and longitudinal associations with weight changes, anorexia of aging, and obesity: results from the NuAge Study.

    Science.gov (United States)

    Roy, Mathieu; Shatenstein, Bryna; Gaudreau, Pierrette; Morais, José A; Payette, Hélène

    2015-03-01

    We examined longitudinal associations between weight dissatisfaction, weight changes, anorexia of aging, and obesity among 1,793 seniors followed over 4 years between 2003 and 2009. Obesity prevalence (body mass index [BMI] ≥ 30) and prevalence/incidence of weight dissatisfaction, anorexia of aging (self-reported appetite loss), and weight changes ≥5% were assessed. Predictors of weight loss ≥5%, anorexia of aging, and weight dissatisfaction were examined using logistic regressions. Half of seniors experienced weight dissatisfaction (50.6%, 95% confidence interval [CI] = [48.1, 53.1]). Anorexia of aging and obesity prevalence was 7.0% (95% CI = [5.7, 8.3]) and 25.1% (95% CI = [22.9, 27.3]), whereas incidence of weight gain/loss ≥5% was 6.6% (95% CI = [1.3, 11.9]) and 8.8% (95% CI = [3.3, 14.3]). Weight gain ≥5% predicts men's subsequent weight dissatisfaction (odds ratio [OR] = 6.66, 95% CI = [2.06, 21.60]). No other association was observed. Weight dissatisfaction is frequent but not associated with subsequent eating disorders. In men, weight gain predicted weight dissatisfaction. Seniors' weight dissatisfaction does not necessarily equate weight changes. Due to its high prevalence, it is of public health interest to understand how seniors' weight dissatisfaction may impact health. © The Author(s) 2014.

  7. Local Prediction Models on Mid-Atlantic Ridge MORB by Principal Component Regression

    Science.gov (United States)

    Ling, X.; Snow, J. E.; Chin, W.

    2017-12-01

    The isotopic compositions of the daughter isotopes of long-lived radioactive systems (Sr, Nd, Hf and Pb ) can be used to map the scale and history of mantle heterogeneities beneath mid-ocean ridges. Our goal is to relate the multidimensional structure in the existing isotopic dataset with an underlying physical reality of mantle sources. The numerical technique of Principal Component Analysis is useful to reduce the linear dependence of the data to a minimum set of orthogonal eigenvectors encapsulating the information contained (cf Agranier et al 2005). The dataset used for this study covers almost all the MORBs along mid-Atlantic Ridge (MAR), from 54oS to 77oN and 8.8oW to -46.7oW, including replicating the dataset of Agranier et al., 2005 published plus 53 basalt samples dredged and analyzed since then (data from PetDB). The principal components PC1 and PC2 account for 61.56% and 29.21%, respectively, of the total isotope ratios variability. The samples with similar compositions to HIMU and EM and DM are identified to better understand the PCs. PC1 and PC2 are accountable for HIMU and EM whereas PC2 has limited control over the DM source. PC3 is more strongly controlled by the depleted mantle source than PC2. What this means is that all three principal components have a high degree of significance relevant to the established mantle sources. We also tested the relationship between mantle heterogeneity and sample locality. K-means clustering algorithm is a type of unsupervised learning to find groups in the data based on feature similarity. The PC factor scores of each sample are clustered into three groups. Cluster one and three are alternating on the north and south MAR. Cluster two exhibits on 45.18oN to 0.79oN and -27.9oW to -30.40oW alternating with cluster one. The ridge has been preliminarily divided into 16 sections considering both the clusters and ridge segments. The principal component regression models the section based on 6 isotope ratios and PCs. The

  8. Comparing Self-Report Measures of Internalized Weight Stigma: The Weight Self-Stigma Questionnaire versus the Weight Bias Internalization Scale.

    Directory of Open Access Journals (Sweden)

    Claudia Hübner

    Full Text Available Internalized weight stigma has gained growing interest due to its association with multiple health impairments in individuals with obesity. Especially high internalized weight stigma is reported by individuals undergoing bariatric surgery. For assessing this concept, two different self-report questionnaires are available, but have never been compared: the Weight Self-Stigma Questionnaire (WSSQ and the Weight Bias Internalization Scale (WBIS. The purpose of the present study was to provide and to compare reliability, convergent validity with and predictive values for psychosocial health outcomes for the WSSQ and WBIS.The WSSQ and the WBIS were used to assess internalized weight stigma in N = 78 prebariatric surgery patients. Further, body mass index (BMI was assessed and body image, quality of life, self-esteem, depression, and anxiety were measured by well-established self-report questionnaires. Reliability, correlation, and regression analyses were conducted.Internal consistency of the WSSQ was acceptable, while good internal consistency was found for the WBIS. Both measures were significantly correlated with each other and body image. While only the WSSQ was correlated with overweight preoccupation, only the WBIS was correlated with appearance evaluation. Both measures were not associated with BMI. However, correlation coefficients did not differ between the WSSQ and the WBIS for all associations with validity measures. Further, both measures significantly predicted quality of life, self-esteem, depression, and anxiety, while the WBIS explained significantly more variance than the WSSQ total score for self-esteem.Findings indicate the WSSQ and the WBIS to be reliable and valid assessments of internalized weight stigma in prebariatric surgery patients, although the WBIS showed marginally more favorable results than the WSSQ. For both measures, longitudinal studies on stability and predictive validity are warranted, for example, for weight

  9. Quantile Regression Methods

    DEFF Research Database (Denmark)

    Fitzenberger, Bernd; Wilke, Ralf Andreas

    2015-01-01

    if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...

  10. Unhealthy weight control behaviors mediate the association between weight status and weight-specific health-related quality of life in treatment-seeking youth who are obese.

    Science.gov (United States)

    Lim, Crystal S; Gowey, Marissa A; Cohen, Megan J; Silverstein, Janet; Janicke, David M

    2017-03-01

    Examine whether unhealthy and extreme weight control behaviors (WCBs) mediate the relationship between youth weight status and disease-specific health-related quality of life (HRQOL) in treatment-seeking youth who are overweight and obese (OV/OB). 82 youth 10-17 years of age who were OV/OB and attending an outpatient obesity-related medical appointment completed measures assessing unhealthy and extreme WCBs and disease-specific HRQOL. Parents completed a demographic questionnaire and medical staff measured youth height and weight. Regression analyses revealed that unhealthy WCBs mediated the associations between youth weight status and emotional and social avoidance disease-specific HRQOL, such that higher body mass index (BMI) predicted unhealthy WCBs, which were ultimately associated with poorer emotional and social HRQOL. Mediation analyses were not significant for total, physical, teasing/marginalization, and positive attributes disease-specific HRQOL. In addition, extreme WCBs did not mediate the association between youth weight status and any subscales of the disease-specific HRQOL measure. Weight status is an important predictor of disease-specific HRQOL in OV/OB youth; however, the association with emotional and social HRQOL is partially accounted for by youth engagement in unhealthy WCBs. Clinicians and researchers should assess WCBs and further research should explore and evaluate appropriate intervention strategies to address unhealthy WCBs in pediatric weight management prevention and treatment efforts.

  11. The Impact of Acculturation Level on Weight Status and Weight Outcomes in Hispanic Children.

    Science.gov (United States)

    Moreno, Jennette P; Vaughan, Elizabeth; Hernandez, Daphne; Cameron, Ryan T; Foreyt, John P; Johnston, Craig A

    2016-12-01

    Previous studies revealed that higher levels of acculturation are related to obesity in Hispanic adults. Conflicting findings exist regarding this relationship in children, and little is known about the impact of acculturation on children's success in pediatric weight management programs. The purposes of the study were to (1) examine the relationship between acculturation and overweight/obese weight status and (2) determine the impact of acculturation on the changes in weight status among overweight/obese children 12 and 24 months after having participated in a weight management intervention. This is a secondary analysis of aggregated data from three randomized control trials that occurred between 2005 and 2009. Height, weight, and level of acculturation using the Child Short Scale for Hispanics (C-SASH) were measured in a sample of Hispanic children (n = 559). Logistic regression models were used to study phase 1 (n = 559) and phase 2 (n = 142), controlling for child and family characteristics. Children reporting high levels of acculturation had a 52 % lower odds of being overweight or obese. Among overweight/obese children who participated in the intervention, high levels of acculturation demonstrated greater reductions in standardized body mass index (zBMI) at 24 months. The results of this study indicate a need to tailor weight management programs for Hispanic children who have lower levels of acculturation.

  12. Vaping to lose weight: Predictors of adult e-cigarette use for weight loss or control.

    Science.gov (United States)

    Morean, Meghan E; Wedel, Amelia V

    2017-03-01

    Some traditional cigarette smokers are motivated to smoke to lose weight or control their weight. The current study evaluated whether a subset of adult e-cigarette users reported vaping to lose or control their weight and examined potential predictors of vaping for weight management. Adult e-cigarette users (n=459) who reported wanting to lose weight or maintain their weight completed an anonymous online survey. Participants reported on demographics, vaping frequency, e-cigarette nicotine content, cigarette smoking status, preferred e-cigarette/e-liquid flavors, current weight status (i.e., overweight, underweight), use of dieting strategies associated with anorexia and bulimia, lifetime history of binge eating, self-discipline, and impulse control. Binary logistic regression was used to examine whether vaping for weight loss/control was associated with the aforementioned variables. Participants who reported vaping for weight loss/control (13.5%) were more likely to vape frequently (adjOR=1.15; 95% CI [1.00, 1.31]); be overweight (adjOR=2.80; [1.33, 5.90]); restrict calories (adjOR=2.23; [1.13, 4.42]); have poor impulse control (adjOR=0.59; [0.41, 0.86]); and prefer coffee- (adjOR=2.92; [1.47, 5.80]) or vanilla-flavored e-liquid (adjOR=7.44; [1.56, 36.08]). A subset of adult e-cigarette users reported vaping for weight loss/control, raising concerns about expanded, scientifically unsubstantiated uses of e-cigarettes. Identifying where individuals obtain information about vaping for weight loss (e.g., e-cigarette ads, Internet) and whether weight-related motives promote e-cigarette initiation among e-cigarette naïve individuals is important to informing regulatory efforts. Further research also is needed to better understand the link between e-liquid flavors and weight loss motivations. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Impact of weight loss on survival after chemoradiation for locally advanced head and neck Cancer: secondary results of a randomized phase III trial (SAKK 10/94)

    International Nuclear Information System (INIS)

    Ghadjar, Pirus; Hayoz, Stefanie; Zimmermann, Frank; Bodis, Stephan; Kaul, David; Badakhshi, Harun; Bernier, Jacques; Studer, Gabriela; Plasswilm, Ludwig; Budach, Volker; Aebersold, Daniel M

    2015-01-01

    To analyze the impact of weight loss before and during chemoradiation on survival outcomes in patients with locally advanced head and neck cancer. From 07/1994-07/2000 a total of 224 patients with squamous cell carcinoma of the head and neck were randomized to either hyperfractionated radiation therapy alone or the same radiation therapy combined with two cycles of concomitant cisplatin. The primary endpoint was time to any treatment failure (TTF); secondary endpoints were locoregional recurrence-free survival (LRRFS), distant metastasis-free survival (DMFS) and overall survival (OS). Patient weight was measured 6 months before treatment, at treatment start and treatment end. The proportion of patients with >5% weight loss was 32% before, and 51% during treatment, and the proportion of patients with >10% weight loss was 12% before, and 17% during treatment. After a median follow-up of 9.5 years (range, 0.1 – 15.4 years) weight loss before treatment was associated with decreased TTF, LRRFS, DMFS, cancer specific survival and OS in a multivariable analysis. However, weight loss during treatment was not associated with survival outcomes. Weight loss before and during chemoradiation was commonly observed. Weight loss before but not during treatment was associated with worse survival

  14. Race/Ethnicity, Gender, Weight Status, and Colorectal Cancer Screening

    Directory of Open Access Journals (Sweden)

    Heather Bittner Fagan

    2011-01-01

    The literature on colorectal cancer (CRC screening is contradictory regarding the impact of weight status on CRC screening. This study was intended to determine if CRC screening rates among 2005 National Health Interview Survey (NHIS respondent racial/ethnic and gender subgroups were influenced by weight status. Methods. Univariable and multivariable logistic regression analyses were performed to determine if CRC screening use differed significantly among obese, overweight, and normal-weight individuals in race/ethnic and gender subgroups. Results. Multivariable analyses showed that CRC screening rates did not differ significantly for individuals within these subgroups who were obese or overweight as compared to their normal-weight peers. Conclusion. Weight status does not contribute to disparities in CRC screening in race/ethnicity and gender subgroups.

  15. Low birth weight is not associated with thyroid autoimmunity

    DEFF Research Database (Denmark)

    Brix, Thomas Heiberg; Hansen, Pia Skov; Rudbeck, Annette Beck

    2006-01-01

    CONTEXT: Low birth weight has been proposed as a risk factor for the development of antibodies toward thyroid peroxidase (TPOAb) and thyroglobulin (TgAb) in adult life. However, the association could also be due to genetic or environmental factors affecting both birth weight and the development...... of thyroid autoantibodies. The effect of these confounders can be minimized through investigation of twin pairs. OBJECTIVE AND DESIGN: To examine the impact of low birth weight on the development of thyroid autoimmunity, we studied whether within-twin-cohort and within-twin-pair differences in birth weight......, gestational age, TSH, and smoking) did not change the findings of nonsignificant regression coefficients. CONCLUSION: Low birth weight per se has no evident role in the etiology of thyroid autoimmunity....

  16. A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design.

    Science.gov (United States)

    Meaney, Christopher; Moineddin, Rahim

    2014-01-24

    In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the

  17. Using Incomplete Information for Complete Weight Annotation of Road Networks

    DEFF Research Database (Denmark)

    Yang, Bin; Kaul, Manohar; Jensen, Christian S.

    2014-01-01

    ground-truth travel cost. A general framework is proposed to solve the problem. Specifically, the problem is modeled as a regression problem and solved by minimizing a judiciously designed objective function that takes into account the topology of the road network. In particular, the use of weighted Page......Rank values of edges is explored for assigning appropriate weights to all edges, and the property of directional adjacency of edges is also taken into account to assign weights. Empirical studies with weights capturing travel time and GHG emissions on two road networks offer insight into the design properties...

  18. A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

    Science.gov (United States)

    Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

    2016-04-01

    Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

  19. GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models.

    Science.gov (United States)

    Chen, Wei; Li, Hui; Hou, Enke; Wang, Shengquan; Wang, Guirong; Panahi, Mahdi; Li, Tao; Peng, Tao; Guo, Chen; Niu, Chao; Xiao, Lele; Wang, Jiale; Xie, Xiaoshen; Ahmad, Baharin Bin

    2018-09-01

    The aim of the current study was to produce groundwater spring potential maps using novel ensemble weights-of-evidence (WoE) with logistic regression (LR) and functional tree (FT) models. First, a total of 66 springs were identified by field surveys, out of which 70% of the spring locations were used for training the models and 30% of the spring locations were employed for the validation process. Second, a total of 14 affecting factors including aspect, altitude, slope, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), lithology, normalized difference vegetation index (NDVI), land use, soil, distance to roads, and distance to streams was used to analyze the spatial relationship between these affecting factors and spring occurrences. Multicollinearity analysis and feature selection of the correlation attribute evaluation (CAE) method were employed to optimize the affecting factors. Subsequently, the novel ensembles of the WoE, LR, and FT models were constructed using the training dataset. Finally, the receiver operating characteristic (ROC) curves, standard error, confidence interval (CI) at 95%, and significance level P were employed to validate and compare the performance of three models. Overall, all three models performed well for groundwater spring potential evaluation. The prediction capability of the FT model, with the highest AUC values, the smallest standard errors, the narrowest CIs, and the smallest P values for the training and validation datasets, is better compared to those of other models. The groundwater spring potential maps can be adopted for the management of water resources and land use by planners and engineers. Copyright © 2018 Elsevier B.V. All rights reserved.

  20. length-weight relationhip of freshwater wild fish species

    African Journals Online (AJOL)

    Dr Naeem

    2012-06-21

    Jun 21, 2012 ... Length-weight (LWR) and length-length relationships (LLR) were determined for a freshwater catfish ... Key words: Mystus bleekeri, length-weight relationship, length-length relationship, predictive equations. INTRODUCTION. Mystus bleekeri (freshwater catfish Day, 1877), locally ..... fish farmers, Aquacult.

  1. Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

    Directory of Open Access Journals (Sweden)

    Hailun Wang

    2017-01-01

    Full Text Available Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.

  2. Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000-2015 using quantile and multiple line regression models

    Science.gov (United States)

    Zhao, Wei; Fan, Shaojia; Guo, Hai; Gao, Bo; Sun, Jiaren; Chen, Laiguo

    2016-11-01

    The quantile regression (QR) method has been increasingly introduced to atmospheric environmental studies to explore the non-linear relationship between local meteorological conditions and ozone mixing ratios. In this study, we applied QR for the first time, together with multiple linear regression (MLR), to analyze the dominant meteorological parameters influencing the mean, 10th percentile, 90th percentile and 99th percentile of maximum daily 8-h average (MDA8) ozone concentrations in 2000-2015 in Hong Kong. The dominance analysis (DA) was used to assess the relative importance of meteorological variables in the regression models. Results showed that the MLR models worked better at suburban and rural sites than at urban sites, and worked better in winter than in summer. QR models performed better in summer for 99th and 90th percentiles and performed better in autumn and winter for 10th percentile. And QR models also performed better in suburban and rural areas for 10th percentile. The top 3 dominant variables associated with MDA8 ozone concentrations, changing with seasons and regions, were frequently associated with the six meteorological parameters: boundary layer height, humidity, wind direction, surface solar radiation, total cloud cover and sea level pressure. Temperature rarely became a significant variable in any season, which could partly explain the peak of monthly average ozone concentrations in October in Hong Kong. And we found the effect of solar radiation would be enhanced during extremely ozone pollution episodes (i.e., the 99th percentile). Finally, meteorological effects on MDA8 ozone had no significant changes before and after the 2010 Asian Games.

  3. Regression Phalanxes

    OpenAIRE

    Zhang, Hongyang; Welch, William J.; Zamar, Ruben H.

    2017-01-01

    Tomal et al. (2015) introduced the notion of "phalanxes" in the context of rare-class detection in two-class classification problems. A phalanx is a subset of features that work well for classification tasks. In this paper, we propose a different class of phalanxes for application in regression settings. We define a "Regression Phalanx" - a subset of features that work well together for prediction. We propose a novel algorithm which automatically chooses Regression Phalanxes from high-dimensi...

  4. Stress, Health Risk Behaviors, and Weight Status Among Community College Students.

    Science.gov (United States)

    Pelletier, Jennifer E; Lytle, Leslie A; Laska, Melissa N

    2016-04-01

    The objective of this study was to describe the relationship between stress, weight-related health risk behaviors (e.g., eating behaviors, physical activity, sedentary behavior, sleep, cigarette smoking, and binge drinking), and weight status using cross-sectional data on 2-year community college students enrolled in a randomized controlled weight gain prevention trial. Modified Poisson regression and linear regression were used to examine crude and adjusted cross-sectional associations. Higher stress was associated with higher prevalence of overweight/obesity (crude prevalence ratio [PR] = 1.05; 95% confidence interval [CI: 1.01, 1.09]), though the relationship was no longer statistically significant after controlling for a wide range of weight-related health risk behaviors (adjusted PR = 1.04; 95% CI [1.00, 1.08]). Stress levels were significantly associated with meal skipping and being a current smoker. Future research should investigate the mechanisms through which stress is related to obesity risk and examine the causes of stress among this understudied population to inform the design of appropriate interventions. © 2015 Society for Public Health Education.

  5. Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

    Science.gov (United States)

    Yang, Aiyuan; Yan, Chunxia; Zhu, Feng; Zhao, Zhongmeng; Cao, Zhi

    2013-01-01

    Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR) is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR), which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds. PMID:23984382

  6. Identifying Interacting Genetic Variations by Fish-Swarm Logic Regression

    Directory of Open Access Journals (Sweden)

    Xuanping Zhang

    2013-01-01

    Full Text Available Understanding associations between genotypes and complex traits is a fundamental problem in human genetics. A major open problem in mapping phenotypes is that of identifying a set of interacting genetic variants, which might contribute to complex traits. Logic regression (LR is a powerful multivariant association tool. Several LR-based approaches have been successfully applied to different datasets. However, these approaches are not adequate with regard to accuracy and efficiency. In this paper, we propose a new LR-based approach, called fish-swarm logic regression (FSLR, which improves the logic regression process by incorporating swarm optimization. In our approach, a school of fish agents are conducted in parallel. Each fish agent holds a regression model, while the school searches for better models through various preset behaviors. A swarm algorithm improves the accuracy and the efficiency by speeding up the convergence and preventing it from dropping into local optimums. We apply our approach on a real screening dataset and a series of simulation scenarios. Compared to three existing LR-based approaches, our approach outperforms them by having lower type I and type II error rates, being able to identify more preset causal sites, and performing at faster speeds.

  7. Multifractal temporally weighted detrended cross-correlation analysis to quantify power-law cross-correlation and its application to stock markets

    Science.gov (United States)

    Wei, Yun-Lan; Yu, Zu-Guo; Zou, Hai-Long; Anh, Vo

    2017-06-01

    A new method—multifractal temporally weighted detrended cross-correlation analysis (MF-TWXDFA)—is proposed to investigate multifractal cross-correlations in this paper. This new method is based on multifractal temporally weighted detrended fluctuation analysis and multifractal cross-correlation analysis (MFCCA). An innovation of the method is applying geographically weighted regression to estimate local trends in the nonstationary time series. We also take into consideration the sign of the fluctuations in computing the corresponding detrended cross-covariance function. To test the performance of the MF-TWXDFA algorithm, we apply it and the MFCCA method on simulated and actual series. Numerical tests on artificially simulated series demonstrate that our method can accurately detect long-range cross-correlations for two simultaneously recorded series. To further show the utility of MF-TWXDFA, we apply it on time series from stock markets and find that power-law cross-correlation between stock returns is significantly multifractal. A new coefficient, MF-TWXDFA cross-correlation coefficient, is also defined to quantify the levels of cross-correlation between two time series.

  8. Eigenvector Weighting Function in Face Recognition

    Directory of Open Access Journals (Sweden)

    Pang Ying Han

    2011-01-01

    Full Text Available Graph-based subspace learning is a class of dimensionality reduction technique in face recognition. The technique reveals the local manifold structure of face data that hidden in the image space via a linear projection. However, the real world face data may be too complex to measure due to both external imaging noises and the intra-class variations of the face images. Hence, features which are extracted by the graph-based technique could be noisy. An appropriate weight should be imposed to the data features for better data discrimination. In this paper, a piecewise weighting function, known as Eigenvector Weighting Function (EWF, is proposed and implemented in two graph based subspace learning techniques, namely Locality Preserving Projection and Neighbourhood Preserving Embedding. Specifically, the computed projection subspace of the learning approach is decomposed into three partitions: a subspace due to intra-class variations, an intrinsic face subspace, and a subspace which is attributed to imaging noises. Projected data features are weighted differently in these subspaces to emphasize the intrinsic face subspace while penalizing the other two subspaces. Experiments on FERET and FRGC databases are conducted to show the promising performance of the proposed technique.

  9. Models for predicting objective function weights in prostate cancer IMRT

    International Nuclear Information System (INIS)

    Boutilier, Justin J.; Lee, Taewoo; Craig, Tim; Sharpe, Michael B.; Chan, Timothy C. Y.

    2015-01-01

    Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR

  10. Models for predicting objective function weights in prostate cancer IMRT

    Energy Technology Data Exchange (ETDEWEB)

    Boutilier, Justin J., E-mail: j.boutilier@mail.utoronto.ca; Lee, Taewoo [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario M5S 3G8 (Canada); Craig, Tim [Radiation Medicine Program, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9, Canada and Department of Radiation Oncology, University of Toronto, 148 - 150 College Street, Toronto, Ontario M5S 3S2 (Canada); Sharpe, Michael B. [Radiation Medicine Program, UHN Princess Margaret Cancer Centre, 610 University of Avenue, Toronto, Ontario M5T 2M9 (Canada); Department of Radiation Oncology, University of Toronto, 148 - 150 College Street, Toronto, Ontario M5S 3S2 (Canada); Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5 (Canada); Chan, Timothy C. Y. [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario M5S 3G8, Canada and Techna Institute for the Advancement of Technology for Health, 124 - 100 College Street, Toronto, Ontario M5G 1P5 (Canada)

    2015-04-15

    Purpose: To develop and evaluate the clinical applicability of advanced machine learning models that simultaneously predict multiple optimization objective function weights from patient geometry for intensity-modulated radiation therapy of prostate cancer. Methods: A previously developed inverse optimization method was applied retrospectively to determine optimal objective function weights for 315 treated patients. The authors used an overlap volume ratio (OV) of bladder and rectum for different PTV expansions and overlap volume histogram slopes (OVSR and OVSB for the rectum and bladder, respectively) as explanatory variables that quantify patient geometry. Using the optimal weights as ground truth, the authors trained and applied three prediction models: logistic regression (LR), multinomial logistic regression (MLR), and weighted K-nearest neighbor (KNN). The population average of the optimal objective function weights was also calculated. Results: The OV at 0.4 cm and OVSR at 0.1 cm features were found to be the most predictive of the weights. The authors observed comparable performance (i.e., no statistically significant difference) between LR, MLR, and KNN methodologies, with LR appearing to perform the best. All three machine learning models outperformed the population average by a statistically significant amount over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and dose to the bladder, rectum, CTV, and PTV. When comparing the weights directly, the LR model predicted bladder and rectum weights that had, on average, a 73% and 74% relative improvement over the population average weights, respectively. The treatment plans resulting from the LR weights had, on average, a rectum V70Gy that was 35% closer to the clinical plan and a bladder V70Gy that was 29% closer, compared to the population average weights. Similar results were observed for all other clinical metrics. Conclusions: The authors demonstrated that the KNN and MLR

  11. Weight gain in pregnancy and child weight status from birth to adulthood in the United States.

    Science.gov (United States)

    Leonard, S A; Petito, L C; Rehkopf, D H; Ritchie, L D; Abrams, B

    2017-08-01

    High weight gain in pregnancy has been associated with child adiposity, but few studies have assessed the relationship across childhood or in racially/ethnically diverse populations. The objectives of the study are to test if weight gain in pregnancy is associated with high birthweight and overweight/obesity in early, middle and late childhood and whether these associations differ by maternal race/ethnicity. Mother-child dyads (n = 7539) were included from the National Longitudinal Survey of Youth 1979, a nationally representative cohort study in the USA (1979-2012). Log-binomial regression models were used to analyse associations between weight gain and the outcomes: high birthweight (>4000 g) and overweight/obesity at ages 2-5, 6-11 and 12-19 years. Excessive weight gain was positively associated, and inadequate weight gain was negatively associated with high birthweight after confounder adjustment (P gain was associated with overweight in early, middle and late childhood. These associations were not significant in Hispanics or Blacks although racial/ethnic interaction was only significant ages 12-19 years (P = 0.03). Helping pregnant women gain weight within national recommendations may aid in preventing overweight and obesity across childhood, particularly for non-Hispanic White mothers. © 2016 World Obesity Federation.

  12. Online Support Vector Regression with Varying Parameters for Time-Dependent Data

    International Nuclear Information System (INIS)

    Omitaomu, Olufemi A.; Jeong, Myong K.; Badiru, Adedeji B.

    2011-01-01

    Support vector regression (SVR) is a machine learning technique that continues to receive interest in several domains including manufacturing, engineering, and medicine. In order to extend its application to problems in which datasets arrive constantly and in which batch processing of the datasets is infeasible or expensive, an accurate online support vector regression (AOSVR) technique was proposed. The AOSVR technique efficiently updates a trained SVR function whenever a sample is added to or removed from the training set without retraining the entire training data. However, the AOSVR technique assumes that the new samples and the training samples are of the same characteristics; hence, the same value of SVR parameters is used for training and prediction. This assumption is not applicable to data samples that are inherently noisy and non-stationary such as sensor data. As a result, we propose Accurate On-line Support Vector Regression with Varying Parameters (AOSVR-VP) that uses varying SVR parameters rather than fixed SVR parameters, and hence accounts for the variability that may exist in the samples. To accomplish this objective, we also propose a generalized weight function to automatically update the weights of SVR parameters in on-line monitoring applications. The proposed function allows for lower and upper bounds for SVR parameters. We tested our proposed approach and compared results with the conventional AOSVR approach using two benchmark time series data and sensor data from nuclear power plant. The results show that using varying SVR parameters is more applicable to time dependent data.

  13. Role of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in local dengue epidemics in Taiwan.

    Science.gov (United States)

    Tsai, Pui-Jen; Teng, Hwa-Jen

    2016-11-09

    Aedes mosquitoes in Taiwan mainly comprise Aedes albopictus and Ae. aegypti. However, the species contributing to autochthonous dengue spread and the extent at which it occurs remain unclear. Thus, in this study, we spatially analyzed real data to determine spatial features related to local dengue incidence and mosquito density, particularly that of Ae. albopictus and Ae. aegypti. We used bivariate Moran's I statistic and geographically weighted regression (GWR) spatial methods to analyze the globally spatial dependence and locally regressed relationship between (1) imported dengue incidences and Breteau indices (BIs) of Ae. albopictus, (2) imported dengue incidences and BI of Ae. aegypti, (3) autochthonous dengue incidences and BI of Ae. albopictus, (4) autochthonous dengue incidences and BI of Ae. aegypti, (5) all dengue incidences and BI of Ae. albopictus, (6) all dengue incidences and BI of Ae. aegypti, (7) BI of Ae. albopictus and human population density, and (8) BI of Ae. aegypti and human population density in 348 townships in Taiwan. In the GWR models, regression coefficients of spatially regressed relationships between the incidence of autochthonous dengue and vector density of Ae. aegypti were significant and positive in most townships in Taiwan. However, Ae. albopictus had significant but negative regression coefficients in clusters of dengue epidemics. In the global bivariate Moran's index, spatial dependence between the incidence of autochthonous dengue and vector density of Ae. aegypti was significant and exhibited positive correlation in Taiwan (bivariate Moran's index = 0.51). However, Ae. albopictus exhibited positively significant but low correlation (bivariate Moran's index = 0.06). Similar results were observed in the two spatial methods between all dengue incidences and Aedes mosquitoes (Ae. aegypti and Ae. albopictus). The regression coefficients of spatially regressed relationships between imported dengue cases and Aedes mosquitoes

  14. Does gender affect neonatal hyperbilirubinemia in low-birth-weight infants?

    Science.gov (United States)

    Tioseco, Jennifer A; Aly, Hany; Milner, Josh; Patel, Kantilal; El-Mohandes, Ayman A E

    2005-03-01

    Neonatal mortality and morbidity are gender-biased in low-birth-weight (LBW) infants. The male disadvantage theory has been suggested to be responsible for these maturational differences. To examine the impact of gender on neonatal hyperbilirubinemia. A retrospective observational study. Data on all LBW infants admitted to George Washington University neonatal intensive care unit and surviving for >48 hrs from January 1992 to March 2003 were analyzed. Males and females were compared for gestational age, birth weight, race, Apgar scores at 1 and 5 mins, peak bilirubin levels, sepsis, and intraventricular hemorrhage (IVH). Significant differences were entered in a regression model to detect the influence of gender on bilirubin (Bili). Analysis was repeated after stratification of infants into: group A, <1000 g; group B, 1000-1499 g; and group C, 1500-2499 g. A total of 840 infants were included in this study. When comparing males (n = 407) with females (n = 433), significant differences were detected in birth weight (1,539 +/- 541 vs. 1,428 +/- 549 g; p = .003), IVH (14.2% vs. 9%; p = .025), and Bili (10.1 +/- 3.0 vs. 9.2 +/- 2.8 mg%; p < .001). No differences were detected in gestational age, sepsis, or Apgar 1 and 5. Difference in Bili for the entire group remained significant in the regression model (regression coefficient [RC] = 0.79 +/- 0.22; p < .001). In subgroup analyses: group A Bili (8.4 +/- 2.3 vs. 8.0 +/- 2.0; p = .14) and group B Bili (9.0 +/- 2.1 vs. 9.2 +/- 2.2; p = .51) did not differ in bivariate or multivariate analyses. In group C, Bili was (11.3 +/- 3.1 vs. 10.1 +/- 3.3; p < .001) and remained the only significant difference in the regression model (RC = 1.19 +/- 0.37; p = .001). Bili in LBW infants is significantly higher in males when compared with females. After stratification to birth weight subgroups, significance is retained in the 1500- to 2499-g group after logistic regression analysis. Bili levels in infants <1500 g are influenced more

  15. Regression models for the restricted residual mean life for right-censored and left-truncated data

    DEFF Research Database (Denmark)

    Cortese, Giuliana; Holmboe, Stine A.; Scheike, Thomas H.

    2017-01-01

    The hazard ratios resulting from a Cox's regression hazards model are hard to interpret and to be converted into prolonged survival time. As the main goal is often to study survival functions, there is increasing interest in summary measures based on the survival function that are easier to inter......The hazard ratios resulting from a Cox's regression hazards model are hard to interpret and to be converted into prolonged survival time. As the main goal is often to study survival functions, there is increasing interest in summary measures based on the survival function that are easier...... to interpret than the hazard ratio; the residual mean time is an important example of those measures. However, because of the presence of right censoring, the tail of the survival distribution is often difficult to estimate correctly. Therefore, we consider the restricted residual mean time, which represents...... a partial area under the survival function, given any time horizon τ, and is interpreted as the residual life expectancy up to τ of a subject surviving up to time t. We present a class of regression models for this measure, based on weighted estimating equations and inverse probability of censoring weighted...

  16. TFAP2B -Dietary Protein and Glycemic Index Interactions and Weight Maintenance after Weight Loss in the DiOGenes Trial

    DEFF Research Database (Denmark)

    Stocks, Tanja; Ängquist, Lars Henrik; Hager, Jörg

    2013-01-01

    Background: TFAP2B rs987237 is associated with obesity and has shown interaction with the dietary fat-to-carbohydrate ratio, which has an effect on weight loss. We investigated interactions between rs987237 and protein-to-carbohydrate ratio or glycemic index (GI) in relation to weight maintenance...... percentage from fat: either low-protein/low-GI, low-protein/high-GI, high-protein/low-GI, or high-protein/high-GI diets, or a control diet for a 6-month weight maintenance period. Using linear regression analyses and additive genetic models, we investigated main and dietary interaction effects of TFAP2B rs...... diverge depending on the nutritional state. © 2013 S. Karger AG, Basel....

  17. Examining Spatial Variation in the Effects of Japanese Red Pine (Pinus densiflora on Burn Severity Using Geographically Weighted Regression

    Directory of Open Access Journals (Sweden)

    Hyun-Joo Lee

    2017-05-01

    Full Text Available Burn severity has profound impacts on the response of post-fire forest ecosystems to fire events. Numerous previous studies have reported that burn severity is determined by variables such as meteorological conditions, pre-fire forest structure, and fuel characteristics. An underlying assumption of these studies was the constant effects of environmental variables on burn severity over space, and these analyses therefore did not consider the spatial dimension. This study examined spatial variation in the effects of Japanese red pine (Pinus densiflora on burn severity. Specifically, this study investigated the presence of spatially varying relationships between Japanese red pine and burn severity due to changes in slope and elevation. We estimated conventional ordinary least squares (OLS and geographically weighted regression (GWR models and compared them using three criteria; the coefficients of determination (R2, Akaike information criterion for small samples (AICc, and Moran’s I-value. The GWR model performed considerably better than the OLS model in explaining variation in burn severity. The results provided strong evidence that the effect of Japanese red pine on burn severity was not constant but varied spatially. Elevation was a significant factor in the variation in the effects of Japanese red pine on burn severity. The influence of red pine on burn severity was considerably higher in low-elevation areas but became less important than the other variables in high-elevation areas. The results of this study can be applied to location-specific strategies for forest managers and can be adopted to improve fire simulation models to more realistically mimic the nature of fire behavior.

  18. Advanced statistics: linear regression, part II: multiple linear regression.

    Science.gov (United States)

    Marill, Keith A

    2004-01-01

    The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

  19. Boosted beta regression.

    Directory of Open Access Journals (Sweden)

    Matthias Schmid

    Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.

  20. Live weight and body measurement of Hungarian Thoroughbred broodmares

    Directory of Open Access Journals (Sweden)

    Szabolcs Bene

    2013-09-01

    Full Text Available Live weights and 21 body measurements of 110 adult brood mares from Thoroughbred breed were evaluated in Hungary. Body measurements and some body measure indices were determined. One way ANOVA was used to compare the studs. Regression equations were developed to estimate the live weight from body measurements. Population genetic parameters of the examined traits were estimated. Only few differences among studs, concerning evaluated body measurements, were presented - firstly: body measurements, related to the kilter and nutritional status (hearth girth - were significant. Between the mentioned traits and the live weight medium positive correlation (r = 0.47 - 0.79; P<0.01 was found. For the estimation of live weight with regression model the necessary data are as follows: hearth girth, 2nd width of rump and diagonal length of body. The determination coefficient was 0.80 (P<0.01. Height at withers, of back and at rump (h2 = 0.66, 0.67 and 0.51 showed medium heritability values. The heritability of depth of chest and height of bieler-point were 0.32 and 0.48, respectively. Quite small differences were found between the stallions in most of the body measurements. The live weight and height measurements were exceptions, as here the differences between the sires were slightly higher. As a conclusion it can be stated that the Thoroughbred population in Hungary is quite homogenous in terms of the most important body measurements.

  1. Describing Growth Pattern of Bali Cows Using Non-linear Regression Models

    Directory of Open Access Journals (Sweden)

    Mohd. Hafiz A.W

    2016-12-01

    Full Text Available The objective of this study was to evaluate the best fit non-linear regression model to describe the growth pattern of Bali cows. Estimates of asymptotic mature weight, rate of maturing and constant of integration were derived from Brody, von Bertalanffy, Gompertz and Logistic models which were fitted to cross-sectional data of body weight taken from 74 Bali cows raised in MARDI Research Station Muadzam Shah Pahang. Coefficient of determination (R2 and residual mean squares (MSE were used to determine the best fit model in describing the growth pattern of Bali cows. Von Bertalanffy model was the best model among the four growth functions evaluated to determine the mature weight of Bali cattle as shown by the highest R2 and lowest MSE values (0.973 and 601.9, respectively, followed by Gompertz (0.972 and 621.2, respectively, Logistic (0.971 and 648.4, respectively and Brody (0.932 and 660.5, respectively models. The correlation between rate of maturing and mature weight was found to be negative in the range of -0.170 to -0.929 for all models, indicating that animals of heavier mature weight had lower rate of maturing. The use of non-linear model could summarize the weight-age relationship into several biologically interpreted parameters compared to the entire lifespan weight-age data points that are difficult and time consuming to interpret.

  2. Regression to Causality : Regression-style presentation influences causal attribution

    DEFF Research Database (Denmark)

    Bordacconi, Mats Joe; Larsen, Martin Vinæs

    2014-01-01

    of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

  3. Birth weight and risk of adiposity among adult Inuit in Greenland.

    Directory of Open Access Journals (Sweden)

    Pernille Falberg Rønn

    Full Text Available OBJECTIVE: The Inuit population in Greenland has undergone rapid socioeconomic and nutritional changes simultaneously with an increasing prevalence of obesity. Therefore, the objective was to examine fetal programming as part of the aetiology of obesity among Inuit in Greenland by investigating the association between birth weight and measures of body composition and fat distribution in adulthood. METHODS: The study was based on cross-sectional data from a total of 1,473 adults aged 18-61 years in two population-based surveys conducted in Greenland between 1999-2001 and 2005-2010. Information on birth weight was collected from birth records. Adiposity was assessed by anthropometry, fat mass index (FMI, fat-free mass index (FFMI, and visceral (VAT and subcutaneous adipose tissue (SAT estimated by ultrasound. The associations to birth weight were analyzed using linear regression models and quadratic splines. Analyses were stratified by sex, and adjusted for age, birthplace, ancestry and family history of obesity. RESULTS: Spline analyses showed linear relations between birth weight and adult adiposity. In multiple regression analyses, birth weight was positively associated with BMI, waist circumference, FMI, FFMI and SAT with generally weaker associations among women compared to men. Birth weight was only associated with VAT after additional adjustment for waist circumference and appeared to be specific and inverse for men only. CONCLUSIONS: Higher birth weight among Inuit was associated with adiposity in adulthood. More studies are needed to explore a potential inverse association between birth size and VAT.

  4. Path analysis for selection of feijoa with greater pulp weight

    Directory of Open Access Journals (Sweden)

    Joel Donazzolo

    Full Text Available ABSTRACT: The objective of this paper was to identify the direct and indirect effects of feijoa fruits (Acca sellowiana traitson pulp weight, in order to use these traits in indirect genotypes selection. Fruits of five feijoa plants were collected in Rio Grande do Sul, in the years of 2009, 2010 and 2011. Six traits were evaluated: diameter, length, total weight, pulp weight, peel thickness and number of seeds per fruit. In the path analysis, with or without ridge regression, pulp weight was considered as the basic variable, and the other traits were considered as explanatory variables. Total weight and fruit diameter had high direct effect, and are the main traits associated with pulp weight. These traits may serve as criteria for indirect selection to increase feijoa pulp weight, since they are easy to be measured.

  5. Nutritional intake and weight z-scores in very low birth weight infants in Peru.

    Science.gov (United States)

    Proaño, Alvaro; Aragón, Romina Elena; Rivera, Fabiola; Zegarra, Jaime

    2016-03-29

    To determine the actual nutritional intake of very low birth weight infants and their growth outcome during the first month of life. Additionally, we identified factors that account for a negative neonatal outcome in this population. A case-series study was conducted in a tertiary hospital in Lima, Peru between 2011 and 2012 and the data was obtained from medical records. No feeding protocol was used during this study. Daily fluids, energy and protein intakes were documented and weekly weight z-scores were calculated. A logistic regression analysis was used to identify factors for an adverse outcome, defined as neonatal mortality or extra-uterine growth restriction, during the first 28 days of life. After applying selection criteria, 76 participants were included. The nutritional intakes were similar to standard values seen in the literature, but protein intakes were suboptimal in all of the four weeks. Birth weight z-score was associated with an adverse outcome (p=0.035). It was determined that having a birth weight z-score under -1.09 predicted a negative outcome with an area under the curve of 96.8% [93.5%, 100%] with a 95% confidence interval. Protein intakes are widely deficient in the population of this study. Nevertheless, an adverse outcome during the neonatal period is more associated with a poor birth weight z-score than nutrition-related factors.

  6. Combined effects of prenatal exposures to environmental chemicals on birth weight

    DEFF Research Database (Denmark)

    Govarts, Eva; Remy, Sylvie; Bruckers, Liesbeth

    2016-01-01

    Prenatal chemical exposure has been frequently associated with reduced fetal growth by single pollutant regression models although inconsistent results have been obtained. Our study estimated the effects of exposure to single pollutants and mixtures on birth weight in 248 mother-child pairs...... with cadmium showed the strongest association with birth weight. In conclusion, birth weight was consistently inversely associated with exposure to pollutant mixtures. Chemicals not showing significant associations at single pollutant level contributed to stronger effects when analyzed as mixtures....

  7. THE REGRESSION MODEL OF IRAN LIBRARIES ORGANIZATIONAL CLIMATE

    OpenAIRE

    Jahani, Mohammad Ali; Yaminfirooz, Mousa; Siamian, Hasan

    2015-01-01

    Background: The purpose of this study was to drawing a regression model of organizational climate of central libraries of Iran?s universities. Methods: This study is an applied research. The statistical population of this study consisted of 96 employees of the central libraries of Iran?s public universities selected among the 117 universities affiliated to the Ministry of Health by Stratified Sampling method (510 people). Climate Qual localized questionnaire was used as research tools. For pr...

  8. Behavioural factors related with successful weight loss 15 months post-enrolment in a commercial web-based weight-loss programme.

    Science.gov (United States)

    Neve, Melinda J; Morgan, Philip J; Collins, Clare E

    2012-07-01

    As further understanding is required of what behavioural factors are associated with long-term weight-loss success, the aim of the present study was to determine the prevalence of successful weight loss 15 months post-enrolment in a commercial web-based weight-loss programme and which behavioural factors were associated with success. An online survey was completed 15 months post-enrolment in a commercial web-based weight-loss programme to assess weight-related behaviours and current weight. Participants were classified as successful if they had lost ≥5 % of their starting weight after 15 months. Commercial users of a web-based weight-loss programme. Participants enrolled in the commercial programme between August 2007 and May 2008. Six hundred and seventy-seven participants completed the survey. The median (interquartile range) weight change was -2·7 (-8·2, 1·6) % of enrolment weight, with 37 % achieving ≥5 % weight loss. Multivariate logistic regression analysis found success was associated with frequency of weight self-monitoring, higher dietary restraint score, lower emotional eating score, not skipping meals, not keeping snack foods in the house and eating takeaway foods less frequently. The findings suggest that individuals trying to achieve or maintain ≥5 % weight loss should be advised to regularly weigh themselves, avoid skipping meals or keeping snack foods in the house, limit the frequency of takeaway food consumption, manage emotional eating and strengthen dietary restraint. Strategies to assist individuals make these changes to behaviour should be incorporated within obesity treatments to improve the likelihood of successful weight loss in the long term.

  9. Obesity and the decision tree: predictors of sustained weight loss after bariatric surgery.

    Science.gov (United States)

    Lee, Yi-Chih; Lee, Wei-Jei; Lin, Yang-Chu; Liew, Phui-Ly; Lee, Chia Ko; Lin, Steven C H; Lee, Tian-Shyung

    2009-01-01

    Bariatric surgery is the only long-lasting effective treatment to reduce body weight in morbid obesity. Previous literature in using data mining techniques to predict weight loss in obese patients who have undergone bariatric surgery is limited. This study used initial evaluations before bariatric surgery and data mining techniques to predict weight outcomes in morbidly obese patients seeking surgical treatment. 251 morbidly obese patients undergoing laparoscopic mini-gastric bypass (LMGB) or adjustable gastric banding (LAGB) with complete clinical data at baseline and at two years were enrolled for analysis. Decision Tree, Logistic Regression and Discriminant analysis technologies were used to predict weight loss. Overall classification capability of the designed diagnostic models was evaluated by the misclassification costs. Two hundred fifty-one patients consisting of 68 men and 183 women was studied; with mean age 33 years. Mean +/- SD weight loss at 2 year was 74.5 +/- 16.4 kg. During two years of follow up, two-hundred and five (81.7%) patients had successful weight reduction while 46 (18.3%) were failed to reduce body weight. Operation methods, alanine transaminase (ALT), aspartate transaminase (AST), white blood cell counts (WBC), insulin and hemoglobin A1c (HbA1c) levels were the predictive factors for successful weight reduction. Decision tree model was a better classification models than traditional logistic regression and discriminant analysis in view of predictive accuracies.

  10. Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors.

    Science.gov (United States)

    Yang, Wei; Zhong, Liming; Chen, Yang; Lin, Liyan; Lu, Zhentai; Liu, Shupeng; Wu, Yao; Feng, Qianjin; Chen, Wufan

    2018-04-01

    Attenuation correction for positron-emission tomography (PET)/magnetic resonance (MR) hybrid imaging systems and dose planning for MR-based radiation therapy remain challenging due to insufficient high-energy photon attenuation information. We present a novel approach that uses the learned nonlinear local descriptors and feature matching to predict pseudo computed tomography (pCT) images from T1-weighted and T2-weighted magnetic resonance imaging (MRI) data. The nonlinear local descriptors are obtained by projecting the linear descriptors into the nonlinear high-dimensional space using an explicit feature map and low-rank approximation with supervised manifold regularization. The nearest neighbors of each local descriptor in the input MR images are searched in a constrained spatial range of the MR images among the training dataset. Then the pCT patches are estimated through k-nearest neighbor regression. The proposed method for pCT prediction is quantitatively analyzed on a dataset consisting of paired brain MRI and CT images from 13 subjects. Our method generates pCT images with a mean absolute error (MAE) of 75.25 ± 18.05 Hounsfield units, a peak signal-to-noise ratio of 30.87 ± 1.15 dB, a relative MAE of 1.56 ± 0.5% in PET attenuation correction, and a dose relative structure volume difference of 0.055 ± 0.107% in , as compared with true CT. The experimental results also show that our method outperforms four state-of-the-art methods.

  11. The impact of aging, hearing loss, and body weight on mouse hippocampal redox state, measured in brain slices using fluorescence imaging.

    Science.gov (United States)

    Stebbings, Kevin A; Choi, Hyun W; Ravindra, Aditya; Llano, Daniel Adolfo

    2016-06-01

    The relationships between oxidative stress in the hippocampus and other aging-related changes such as hearing loss, cortical thinning, or changes in body weight are not yet known. We measured the redox ratio in a number of neural structures in brain slices taken from young and aged mice. Hearing thresholds, body weight, and cortical thickness were also measured. We found striking aging-related increases in the redox ratio that were isolated to the stratum pyramidale, while such changes were not observed in thalamus or cortex. These changes were driven primarily by changes in flavin adenine dinucleotide, not nicotinamide adenine dinucleotide hydride. Multiple regression analysis suggested that neither hearing threshold nor cortical thickness independently contributed to this change in hippocampal redox ratio. However, body weight did independently contribute to predicted changes in hippocampal redox ratio. These data suggest that aging-related changes in hippocampal redox ratio are not a general reflection of overall brain oxidative state but are highly localized, while still being related to at least one marker of late aging, weight loss at the end of life. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Partial F-tests with multiply imputed data in the linear regression framework via coefficient of determination.

    Science.gov (United States)

    Chaurasia, Ashok; Harel, Ofer

    2015-02-10

    Tests for regression coefficients such as global, local, and partial F-tests are common in applied research. In the framework of multiple imputation, there are several papers addressing tests for regression coefficients. However, for simultaneous hypothesis testing, the existing methods are computationally intensive because they involve calculation with vectors and (inversion of) matrices. In this paper, we propose a simple method based on the scalar entity, coefficient of determination, to perform (global, local, and partial) F-tests with multiply imputed data. The proposed method is evaluated using simulated data and applied to suicide prevention data. Copyright © 2014 John Wiley & Sons, Ltd.

  13. Psychosocial work environment factors and weight change

    DEFF Research Database (Denmark)

    Gram Quist, Helle; Christensen, Ulla; Christensen, Karl Bang

    2013-01-01

    BACKGROUND: Lifestyle variables may serve as important intermediate factors between psychosocial work environment and health outcomes. Previous studies, focussing on work stress models have shown mixed and weak results in relation to weight change. This study aims to investigate psychosocial...... factors outside the classical work stress models as potential predictors of change in body mass index (BMI) in a population of health care workers. METHODS: A cohort study, with three years follow-up, was conducted among Danish health care workers (3982 women and 152 men). Logistic regression analyses...... predicted weight loss among men. Associations were generally weak, with the exception of quality of leadership, age, and cohabitation. CONCLUSION: This study of a single occupational group suggested a few new risk factors for weight change outside the traditional work stress models....

  14. Demonstration of a Fiber Optic Regression Probe in a High-Temperature Flow

    Science.gov (United States)

    Korman, Valentin; Polzin, Kurt

    2011-01-01

    The capability to provide localized, real-time monitoring of material regression rates in various applications has the potential to provide a new stream of data for development testing of various components and systems, as well as serving as a monitoring tool in flight applications. These applications include, but are not limited to, the regression of a combusting solid fuel surface, the ablation of the throat in a chemical rocket or the heat shield of an aeroshell, and the monitoring of erosion in long-life plasma thrusters. The rate of regression in the first application is very fast, while the second and third are increasingly slower. A recent fundamental sensor development effort has led to a novel regression, erosion, and ablation sensor technology (REAST). The REAST sensor allows for measurement of real-time surface erosion rates at a discrete surface location. The sensor is optical, using two different, co-located fiber-optics to perform the regression measurement. The disparate optical transmission properties of the two fiber-optics makes it possible to measure the regression rate by monitoring the relative light attenuation through the fibers. As the fibers regress along with the parent material in which they are embedded, the relative light intensities through the two fibers changes, providing a measure of the regression rate. The optical nature of the system makes it relatively easy to use in a variety of harsh, high temperature environments, and it is also unaffected by the presence of electric and magnetic fields. In addition, the sensor could be used to perform optical spectroscopy on the light emitted by a process and collected by fibers, giving localized measurements of various properties. The capability to perform an in-situ measurement of material regression rates is useful in addressing a variety of physical issues in various applications. An in-situ measurement allows for real-time data regarding the erosion rates, providing a quick method for

  15. Who will lose weight? A reexamination of predictors of weight loss in women

    Directory of Open Access Journals (Sweden)

    Barata José T

    2004-08-01

    Full Text Available Abstract Background The purpose of this study was to analyze pretreatment predictors of short-term weight loss in Portuguese overweight and obese women involved in a weight management program. Behavioral and psychosocial predictors were selected a priori from previous results reported in American women who participated in a similar program. Methods Subjects were 140 healthy overweight/obese women (age, 38.3 ± 5.9 y; BMI, 30.3 ± 3.7 kg/m2 who participated in a 4-month lifestyle weight loss program consisting of group-based behavior therapy to improve diet and increase physical activity. At baseline, all women completed a comprehensive behavioral and psychosocial battery, in standardized conditions. Results Of all starting participants, 3.5% (5 subjects did not finish the program. By treatment's end, more than half of all women had met the recomended weight loss goals, despite a large variability in individual results (range for weight loss = 19 kg. In bivariate and multivariate correlation/regression analysis fewer previous diets and weight outcome evaluations, and to a lesser extent self-motivation and body image were significant and independent predictors of weight reduction, before and after adjustment for baseline weight. A negative and slightly curvilinear relationship best described the association between outcome evaluations and weight change, revealing that persons with very accepting evaluations (that would accept or be happy with minimal weight change lost the least amount of weight while positive but moderate evaluations of outcomes (i.e., neither low nor extremely demanding were more predictive of success. Among those subjects who reported having initiated more than 3–4 diets in the year before the study, very few were found to be in the most successful group after treatment. Quality of life, self-esteem, and exercise variables did not predict outcomes. Conclusions Several variables were confirmed as predictors of success in short

  16. A longitudinal study to explain strategies to change weight and muscles among normal weight and overweight children.

    Science.gov (United States)

    McCabe, M P; Ricciardelli, L A; Holt, K

    2005-12-01

    Previous research has indicated that both boys and girls strive for a slim body, with boys having an additional focus on a muscular body build. The current study was designed to evaluate the utility of a biopsychosocial model to explain body image and body change strategies among children. The study evaluated changes over time in body image and strategies to lose weight and increase muscles among 132 normal weight and 67 overweight boys (mean age = 9.23 years) and 158 normal weight and 55 overweight girls (mean age = 9.33 years). The predictive role of BMI, positive and negative affect, self-esteem and perceived sociocultural pressures to lose weight or increase muscle on body image and body change strategies over a 16 month period was evaluated. All participants completed the questionnaire on both occasions. The results demonstrated that both overweight boys and girls were more likely to be dissatisfied with their weight, place more importance on their weight, engage in more strategies to lose weight as well as perceive more pressure to lose weight. Overweight boys and girls were also more likely to report lower levels of self-esteem and positive affect, and higher levels of negative affect, and reported a reduction in their self-esteem over time. Regression analyses demonstrated that among overweight boys, low self-esteem and high levels of perceived pressure to lose weight predicted weight dissatisfaction; for overweight girls, weight dissatisfaction was also predicted by low levels of self-esteem. The implication of these findings in terms of factors contributing to the adoption of health risk behaviors among children is discussed.

  17. Using within-day hive weight changes to measure environmental effects on honey bee colonies

    Science.gov (United States)

    Patterns in within-day hive weight data from two independent datasets in Arizona and California were modeled using piecewise regression, and analyzed with respect to honey bee colony behavior and landscape effects. The regression analysis yielded information on the start and finish of a colony’s dai...

  18. What Matters in Weight Loss? An In-Depth Analysis of Self-Monitoring.

    Science.gov (United States)

    Painter, Stefanie Lynn; Ahmed, Rezwan; Hill, James O; Kushner, Robert F; Lindquist, Richard; Brunning, Scott; Margulies, Amy

    2017-05-12

    Using technology to self-monitor body weight, dietary intake, and physical activity is a common practice used by consumers and health companies to increase awareness of current and desired behaviors in weight loss. Understanding how to best use the information gathered by these relatively new methods needs to be further explored. The purpose of this study was to analyze the contribution of self-monitoring to weight loss in participants in a 6-month commercial weight-loss intervention administered by Retrofit and to specifically identify the significant contributors to weight loss that are associated with behavior and outcomes. A retrospective analysis was performed using 2113 participants enrolled from 2011 to 2015 in a Retrofit weight-loss program. Participants were males and females aged 18 years or older with a starting body mass index of ≥25 kg/m2, who also provided a weight measurement at the sixth month of the program. Multiple regression analysis was performed using all measures of self-monitoring behaviors involving weight measurements, dietary intake, and physical activity to predict weight loss at 6 months. Each significant predictor was analyzed in depth to reveal the impact on outcome. Participants in the Retrofit Program lost a mean -5.58% (SE 0.12) of their baseline weight with 51.87% (1096/2113) of participants losing at least 5% of their baseline weight. Multiple regression model (R 2 =.197, Pself-monitoring behaviors of self-weigh-in, daily steps, high-intensity activity, and persistent food logging were significant predictors of weight loss during a 6-month intervention. ©Stefanie Lynn Painter, Rezwan Ahmed, James O Hill, Robert F Kushner, Richard Lindquist, Scott Brunning, Amy Margulies. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 12.05.2017.

  19. Convergence diagnostics for Eigenvalue problems with linear regression model

    International Nuclear Information System (INIS)

    Shi, Bo; Petrovic, Bojan

    2011-01-01

    Although the Monte Carlo method has been extensively used for criticality/Eigenvalue problems, a reliable, robust, and efficient convergence diagnostics method is still desired. Most methods are based on integral parameters (multiplication factor, entropy) and either condense the local distribution information into a single value (e.g., entropy) or even disregard it. We propose to employ the detailed cycle-by-cycle local flux evolution obtained by using mesh tally mechanism to assess the source and flux convergence. By applying a linear regression model to each individual mesh in a mesh tally for convergence diagnostics, a global convergence criterion can be obtained. We exemplify this method on two problems and obtain promising diagnostics results. (author)

  20. Estimation of Covariance Matrix on Bi-Response Longitudinal Data Analysis with Penalized Spline Regression

    Science.gov (United States)

    Islamiyati, A.; Fatmawati; Chamidah, N.

    2018-03-01

    The correlation assumption of the longitudinal data with bi-response occurs on the measurement between the subjects of observation and the response. It causes the auto-correlation of error, and this can be overcome by using a covariance matrix. In this article, we estimate the covariance matrix based on the penalized spline regression model. Penalized spline involves knot points and smoothing parameters simultaneously in controlling the smoothness of the curve. Based on our simulation study, the estimated regression model of the weighted penalized spline with covariance matrix gives a smaller error value compared to the error of the model without covariance matrix.

  1. SU-F-J-64: Comparison of Dosimetric Robustness Between Proton Therapy and IMRT Plans Following Tumor Regression for Locally Advanced Non-Small Cell Lung Cancer (NSCLC)

    Energy Technology Data Exchange (ETDEWEB)

    Teng, C; Ainsley, C; Teo, B; Burgdorf, B; Berman, A; Levin, W; Xiao, Y; Lin, L; Simone, C; Solberg, T [University of Pennsylvania, Philadelphia, PA (United States); Janssens, G [IBA, Louvain-la-Neuve (Belgium)

    2016-06-15

    Purpose: In the light of tumor regression and normal tissue changes, dose distributions can deviate undesirably from what was planned. As a consequence, replanning is sometimes necessary during treatment to ensure continued tumor coverage or to avoid overdosing organs at risk (OARs). Proton plans are generally thought to be less robust than photon plans because of the proton beam’s higher sensitivity to changes in tissue composition, suggesting also a higher likely replanning rate due to tumor regression. The purpose of this study is to compare dosimetric deviations between forward-calculated double scattering (DS) proton plans with IMRT plans upon tumor regression, and assesses their impact on clinical replanning decisions. Methods: Ten consecutive locally advanced NSCLC patients whose tumors shrank > 50% in volume and who received four or more CT scans during radiotherapy were analyzed. All the patients received proton radiotherapy (6660 cGy, 180 cGy/fx). Dosimetric robustness during therapy was characterized by changes in the planning objective metrics as well as by point-by-point root-mean-squared differences for the entire PTV, ITV, and OARs (heart, cord, esophagus, brachial plexus and lungs) DVHs. Results: Sixty-four pairs of DVHs were reviewed by three clinicians, who requested a replanning rate of 16.7% and 18.6% for DS and IMRT plans, respectively, with a high agreement between providers. Robustness of clinical indicators was found to depend on the beam orientation and dose level on the DVH curve. Proton dose increased most in OARs distal to the PTV along the beam path, but these changes were primarily in the mid to low dose levels. In contrast, the variation in IMRT plans occurred primarily in the high dose region. Conclusion: Robustness of clinical indicators depends where on the DVH curves comparisons are made. Similar replanning rates were observed for DS and IMRT plans upon large tumor regression.

  2. Weighing every day matters: daily weighing improves weight loss and adoption of weight control behaviors.

    Science.gov (United States)

    Steinberg, Dori M; Bennett, Gary G; Askew, Sandy; Tate, Deborah F

    2015-04-01

    Daily weighing is emerging as the recommended self-weighing frequency for weight loss. This is likely because it improves adoption of weight control behaviors. To examine whether weighing every day is associated with greater adoption of weight control behaviors compared with less frequent weighing. Longitudinal analysis of a previously conducted 6-month randomized controlled trial. Overweight men and women in Chapel Hill, NC, participated in the intervention arm (N=47). The intervention focused on daily weighing for weight loss using an e-scale that transmitted weights to a study website, along with weekly e-mailed lessons and tailored feedback on daily weighing adherence and weight loss progress. We gathered objective data on self-weighing frequency from the e-scales. At baseline and 6 months, weight change was measured in the clinic and weight control behaviors (total items=37), dietary strategies, and calorie expenditure from physical activity were assessed via questionnaires. Calorie intake was assessed using an online 24-hour recall tool. We used χ(2) tests to examine variation in discrete weight control behaviors and linear regression models to examine differences in weight, dietary strategies, and calorie intake and expenditure by self-weighing frequency. Fifty-one percent of participants weighed every day (n=24) over 6 months. The average self-weighing frequency among those weighing less than daily (n=23) was 5.4±1.2 days per week. Daily weighers lost significantly more weight compared with those weighing less than daily (mean difference=-6.1 kg; 95% CI -10.2 to -2.1; P=0.004). The total number of weight control behaviors adopted was greater among daily weighers (17.6±7.6 vs 11.2±6.4; P=0.004). There were no differences by self-weighing frequency in dietary strategies, calorie intake, or calorie expenditure. Weighing every day led to greater adoption of weight control behaviors and produced greater weight loss compared with weighing most days of the

  3. Birth weight and neonatal adiposity prediction using fractional limb volume obtained with 3D ultrasound.

    Science.gov (United States)

    O'Connor, Clare; O'Higgins, Amy; Doolan, Anne; Segurado, Ricardo; Stuart, Bernard; Turner, Michael J; Kennelly, Máireád M

    2014-01-01

    The objective of this investigation was to study fetal thigh volume throughout gestation and explore its correlation with birth weight and neonatal body composition. This novel technique may improve birth weight prediction and lead to improved detection rates for fetal growth restriction. Fractional thigh volume (TVol) using 3D ultrasound, fetal biometry and soft tissue thickness were studied longitudinally in 42 mother-infant pairs. The percentages of neonatal body fat, fat mass and fat-free mass were determined using air displacement plethysmography. Correlation and linear regression analyses were performed. Linear regression analysis showed an association between TVol and birth weight. TVol at 33 weeks was also associated with neonatal fat-free mass. There was no correlation between TVol and neonatal fat mass. Abdominal circumference, estimated fetal weight (EFW) and EFW centile showed consistent correlations with birth weight. Thigh volume demonstrated an additional independent contribution to birth weight prediction when added to the EFW centile from the 38-week scan (p = 0.03). Fractional TVol performed at 33 weeks gestation is correlated with birth weight and neonatal lean body mass. This screening test may highlight those at risk of fetal growth restriction or macrosomia.

  4. Weight misperception and disordered weight control behaviors among U.S. high school students with overweight and obesity: Associations and trends, 1999-2013.

    Science.gov (United States)

    Hazzard, Vivienne M; Hahn, Samantha L; Sonneville, Kendrin R

    2017-08-01

    To examine prevalence of weight misperception (incongruence between one's perceived weight status and one's actual weight status) and disordered weight control behaviors (DWCBs; unhealthy behaviors aiming to control or modify weight), associations between weight misperception and DWCBs, and temporal trends in prevalence and associations among adolescents with overweight and obesity from 1999 to 2013. Self-reported data from eight biennial cycles (1999-2013) of the cross-sectional national Youth Risk Behavior Survey were used in analyses restricted to respondents with overweight/obesity. Data on weight status perception, use of fasting, purging, and diet pills to control weight, sex, race/ethnicity, and grade in school were used in multivariate logistic regression models. Among U.S. high school students with overweight and obesity, no linear temporal trends were detected for prevalence of weight misperception, fasting, or purging between 1999 and 2013, while a significant linear decrease was observed for prevalence of diet pill use between 1999 and 2013 (b=-0.81, pfasting to control weight among males. No significant changes over time in associations of weight misperception with fasting or purging were observed, though the association between weight misperception and diet pill use weakened somewhat across 1999-2013. In the context of increasing prevalence of overweight and obesity, weight misperception appears to be a robust protective factor for DWCBs. Copyright © 2017. Published by Elsevier Ltd.

  5. Squeezeposenet: Image Based Pose Regression with Small Convolutional Neural Networks for Real Time Uas Navigation

    Science.gov (United States)

    Müller, M. S.; Urban, S.; Jutzi, B.

    2017-08-01

    The number of unmanned aerial vehicles (UAVs) is increasing since low-cost airborne systems are available for a wide range of users. The outdoor navigation of such vehicles is mostly based on global navigation satellite system (GNSS) methods to gain the vehicles trajectory. The drawback of satellite-based navigation are failures caused by occlusions and multi-path interferences. Beside this, local image-based solutions like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) can e.g. be used to support the GNSS solution by closing trajectory gaps but are computationally expensive. However, if the trajectory estimation is interrupted or not available a re-localization is mandatory. In this paper we will provide a novel method for a GNSS-free and fast image-based pose regression in a known area by utilizing a small convolutional neural network (CNN). With on-board processing in mind, we employ a lightweight CNN called SqueezeNet and use transfer learning to adapt the network to pose regression. Our experiments show promising results for GNSS-free and fast localization.

  6. Influence of water-filtered infrared-A (wIRA on reduction of local fat and body weight by physical exercise

    Directory of Open Access Journals (Sweden)

    Schmitz, Gerd

    2006-07-01

    Full Text Available Aim of the study: Investigation, whether water-filtered infrared-A (wIRA irradiation during moderate bicycle ergometer endurance exercise has effects especially on local fat reduction and on weight reduction beyond the effects of ergometer exercise alone. Methods: Randomised controlled study with 40 obese females (BMI 30-40 (median: 34.5, body weight 76-125 (median: 94.9 kg, age 20-40 (median: 35.5 years, isocaloric nutrition, 20 in the wIRA group and 20 in the control group. In both groups each participant performed 3 times per week over 4 weeks for 45 minutes bicycle ergometer endurance exercise with a constant load according to a lactate level of 2 mmol/l (aerobic endurance load, as determined before the intervention period. In the wIRA group in addition large parts of the body (including waist, hip, and thighs were irradiated during all ergometries of the intervention period with visible light and a predominant part of water-filtered infrared-A (wIRA, using the irradiation unit “Hydrosun® 6000” with 10 wIRA radiators (Hydrosun® Medizintechnik, Müllheim, Germany, radiator type 500, 4 mm water cuvette, yellow filter, water-filtered spectrum 500-1400 nm around a speed independent bicycle ergometer. Main variable of interest: change of “the sum of circumferences of waist, hip, and both thighs of each patient” over the intervention period (4 weeks. Additional variables of interest: body weight, body mass index BMI, body fat percentage, fat mass, fat-free mass, water mass (analysis of body composition by tetrapolar bioimpedance analysis, assessment of an arteriosclerotic risk profile by blood investigation of variables of lipid metabolism (cholesterol, triglycerides, high density lipoproteins HDL, low density lipoproteins LDL, apolipoprotein A1, apolipoprotein B, clinical chemistry (fasting glucose, alanin-aminotransferase ALT (= glutamyl pyruvic transaminase GPT, gamma-glutamyl-transferase GGT, creatinine, albumin, endocrinology

  7. An improved partial least-squares regression method for Raman spectroscopy

    Science.gov (United States)

    Momenpour Tehran Monfared, Ali; Anis, Hanan

    2017-10-01

    It is known that the performance of partial least-squares (PLS) regression analysis can be improved using the backward variable selection method (BVSPLS). In this paper, we further improve the BVSPLS based on a novel selection mechanism. The proposed method is based on sorting the weighted regression coefficients, and then the importance of each variable of the sorted list is evaluated using root mean square errors of prediction (RMSEP) criterion in each iteration step. Our Improved BVSPLS (IBVSPLS) method has been applied to leukemia and heparin data sets and led to an improvement in limit of detection of Raman biosensing ranged from 10% to 43% compared to PLS. Our IBVSPLS was also compared to the jack-knifing (simpler) and Genetic Algorithm (more complex) methods. Our method was consistently better than the jack-knifing method and showed either a similar or a better performance compared to the genetic algorithm.

  8. Nitrogen dioxide concentrations in neighborhoods adjacent to a commercial airport: a land use regression modeling study.

    Science.gov (United States)

    Adamkiewicz, Gary; Hsu, Hsiao-Hsien; Vallarino, Jose; Melly, Steven J; Spengler, John D; Levy, Jonathan I

    2010-11-17

    There is growing concern in communities surrounding airports regarding the contribution of various emission sources (such as aircraft and ground support equipment) to nearby ambient concentrations. We used extensive monitoring of nitrogen dioxide (NO2) in neighborhoods surrounding T.F. Green Airport in Warwick, RI, and land-use regression (LUR) modeling techniques to determine the impact of proximity to the airport and local traffic on these concentrations. Palmes diffusion tube samplers were deployed along the airport's fence line and within surrounding neighborhoods for one to two weeks. In total, 644 measurements were collected over three sampling campaigns (October 2007, March 2008 and June 2008) and each sampling location was geocoded. GIS-based variables were created as proxies for local traffic and airport activity. A forward stepwise regression methodology was employed to create general linear models (GLMs) of NO2 variability near the airport. The effect of local meteorology on associations with GIS-based variables was also explored. Higher concentrations of NO2 were seen near the airport terminal, entrance roads to the terminal, and near major roads, with qualitatively consistent spatial patterns between seasons. In our final multivariate model (R2 = 0.32), the local influences of highways and arterial/collector roads were statistically significant, as were local traffic density and distance to the airport terminal (all p GIS variables, and the regression model structure was robust to various model-building approaches. Our study has shown that there are clear local variations in NO2 in the neighborhoods that surround an urban airport, which are spatially consistent across seasons. LUR modeling demonstrated a strong influence of local traffic, except the smallest roads that predominate in residential areas, as well as proximity to the airport terminal.

  9. Parenting style as a predictor of adolescent weight and weight-related behaviors.

    Science.gov (United States)

    Berge, Jerica M; Wall, Melanie; Loth, Katie; Neumark-Sztainer, Dianne

    2010-04-01

    Current research indicates that specific parenting styles are associated with adolescent overweight, dietary intake, and physical activity; but most of the research has been cross-sectional, making it difficult to determine the temporal order of these associations. The current study adds to the previous research by examining 5-year longitudinal associations between parenting style and adolescent weight and weight-related behaviors. Data from Project EAT, a population-based study with adolescents from diverse ethnic and socioeconomic backgrounds, were used. Adolescents (N = 2,516) from 31 Minnesota schools completed in-class assessments in 1999 (Time 1) and mailed surveys in 2004 (Time 2). Multiple linear regression models were used to predict mean levels of adolescent outcomes at Time 2 from parenting style at Time 1. Time 1 maternal authoritative parenting style predicted lower body mass index in adolescent sons and daughters at Time 2. Time 1 paternal permissive parenting style predicted more fruits and vegetables intake in daughters at Time 2. Significant associations were not found between parenting style and adolescent physical activity. Findings suggest that authoritative parenting style may play a protective role related to adolescent overweight and that the dimension of warmth and/or caring in the parent-adolescent relationship may be important in relation to female adolescent healthy dietary intake. Further exploration of opposite sex parent-adolescent dyad patterns related to parenting style and adolescent weight and weight-related behaviors is warranted. Copyright 2010 Society for Adolescent Medicine. Published by Elsevier Inc. All rights reserved.

  10. Weight monitoring system for newborn incubator application

    Science.gov (United States)

    Widianto, Arif; Nurfitri, Intan; Mahatidana, Pradipta; Abuzairi, Tomy; Poespawati, N. R.; Purnamaningsih., Retno W.

    2018-02-01

    We proposed weight monitoring system using load cell sensor for newborn incubator application. The weight sensing system consists of a load cell, conditioning signal circuit, and microcontroller Arduino Uno R3. The performance of the sensor was investigated by using the various weight from 0 up to 3000 g. Experiment results showed that this system has a small error of 4.313% and 12.5 g of threshold and resolution value. Compared to the typical baby scale available in local market, the proposed system has a lower error value and hysteresis.

  11. Regression analysis with categorized regression calibrated exposure: some interesting findings

    Directory of Open Access Journals (Sweden)

    Hjartåker Anette

    2006-07-01

    Full Text Available Abstract Background Regression calibration as a method for handling measurement error is becoming increasingly well-known and used in epidemiologic research. However, the standard version of the method is not appropriate for exposure analyzed on a categorical (e.g. quintile scale, an approach commonly used in epidemiologic studies. A tempting solution could then be to use the predicted continuous exposure obtained through the regression calibration method and treat it as an approximation to the true exposure, that is, include the categorized calibrated exposure in the main regression analysis. Methods We use semi-analytical calculations and simulations to evaluate the performance of the proposed approach compared to the naive approach of not correcting for measurement error, in situations where analyses are performed on quintile scale and when incorporating the original scale into the categorical variables, respectively. We also present analyses of real data, containing measures of folate intake and depression, from the Norwegian Women and Cancer study (NOWAC. Results In cases where extra information is available through replicated measurements and not validation data, regression calibration does not maintain important qualities of the true exposure distribution, thus estimates of variance and percentiles can be severely biased. We show that the outlined approach maintains much, in some cases all, of the misclassification found in the observed exposure. For that reason, regression analysis with the corrected variable included on a categorical scale is still biased. In some cases the corrected estimates are analytically equal to those obtained by the naive approach. Regression calibration is however vastly superior to the naive method when applying the medians of each category in the analysis. Conclusion Regression calibration in its most well-known form is not appropriate for measurement error correction when the exposure is analyzed on a

  12. Obesity Might Be a Predictor of Weight Reduction after Smoking Cessation

    DEFF Research Database (Denmark)

    Pisinger, Charlotta; Nielsen, Helle Øster; Kuhlmann, Caroline

    2017-01-01

    Background and Objectives: Approximately one in five ex-smokers reduces or maintains weight after smoking cessation but little is known about who succeeds to avoid weight gain. The purpose of this study was to identify predictors of weight reduction after long-term smoking cessation in a general...... population. Methods: Data was obtained from two Danish population-based cohorts (the Inter99 and the Helbred2006 study). Anthropometric measurements were performed by trained research staff. Out of 3.577 daily smokers at baseline 317 participants had quit smoking at the five-year follow-up for at least one...... year. Multiple logistic regression analysis was performed to determine predictors of weight reduction. Results: Thirteen percent reduced weight by at least 1 kg and 4% maintained their weight. Quitters with obesity had more than seven times higher odds than normal weight quitters to lose weight (OR 7...

  13. Does the Magnitude of the Link between Unemployment and Crime Depend on the Crime Level? A Quantile Regression Approach

    Directory of Open Access Journals (Sweden)

    Horst Entorf

    2015-07-01

    Full Text Available Two alternative hypotheses – referred to as opportunity- and stigma-based behavior – suggest that the magnitude of the link between unemployment and crime also depends on preexisting local crime levels. In order to analyze conjectured nonlinearities between both variables, we use quantile regressions applied to German district panel data. While both conventional OLS and quantile regressions confirm the positive link between unemployment and crime for property crimes, results for assault differ with respect to the method of estimation. Whereas conventional mean regressions do not show any significant effect (which would confirm the usual result found for violent crimes in the literature, quantile regression reveals that size and importance of the relationship are conditional on the crime rate. The partial effect is significantly positive for moderately low and median quantiles of local assault rates.

  14. Improvement of Zinc Coating Weight Control for Transition of Target Change

    Energy Technology Data Exchange (ETDEWEB)

    Chen, Chien Ming; Lin, Jeong Hwa; Hsu, Tse Wei; Lin, Rui Rong [China Steel Corporation, Kaohsiung (China)

    2010-06-15

    The product specification of the Continuous Hot Dip Galvanizing Line (CGL) changes and varies constantly with different customers' requirements, especially in the zinc coating weight which is from 30 to 150 g/m{sup 2} on each side. Since the coating weight of zinc changes often, it is very important to reduce time spent in the transfer of target values changed for low production cost and yield loss. The No.2 CGL in China Steel Corporation (CSC) has improved the control of the air knife which is designed by Siemens VAI. CSC proposed an experiment design which is an L{sub 9}(3{sup 4}) orthogonal array to find the relations between zinc coating weight and the process parameters, such as the line speed, air pressure, gap of air knife and air knife position. A non-linear regression formula was derived from the experimental results and applied in the mathematical model. A new air knife feedforward control system, which is coupled with the regression formula, the air knife control system and the process computer, is implemented into the line. The practical plant operation results have been presented to show the transfer time is obviously shortened while zinc coating weight target changing and the product rejected ratio caused by zinc coating weight out of specification is significantly reduced from 0.5% to 0.15%.

  15. [Association between hours of television watched, physical activity, sleep and excess weight among young adults].

    Science.gov (United States)

    Martínez-Moyá, María; Navarrete-Muñoz, Eva M; García de la Hera, Manuela; Giménez-Monzo, Daniel; González-Palacios, Sandra; Valera-Gran, Desirée; Sempere-Orts, María; Vioque, Jesús

    2014-01-01

    To explore the association between excess weight or body mass index (BMI) and the time spent watching television, self-reported physical activity and sleep duration in a young adult population. We analyzed cross-sectional baseline data of 1,135 participants (17-35 years old) from the project Dieta, salud y antropometría en población universitaria (Diet, Health and Anthrompmetric Variables in Univeristy Students). Information about time spent watching television, sleep duration, self-reported physical activity and self-reported height and weight was provided by a baseline questionnaire. BMI was calculated as kg/m(2) and excess of weight was defined as ≥25. We used multiple logistic regression to explore the association between excess weight (no/yes) and independent variables, and multiple linear regression for BMI. The prevalence of excess weight was 13.7% (11.2% were overweight and 2.5% were obese). A significant positive association was found between excess weight and a greater amount of time spent watching television. Participants who reported watching television >2h a day had a higher risk of excess weight than those who watched television ≤1h a day (OR=2.13; 95%CI: 1.37-3.36; p-trend: 0.002). A lower level of physical activity was associated with an increased risk of excess weight, although the association was statistically significant only in multiple linear regression (p=0.037). No association was observed with sleep duration. A greater number of hours spent watching television and lower physical activity were significantly associated with a higher BMI in young adults. Both factors are potentially modifiable with preventive strategies. Copyright © 2013 SESPAS. Published by Elsevier Espana. All rights reserved.

  16. La formule des traces locale tordue

    CERN Document Server

    Moeglin, Colette

    2018-01-01

    A note to readers: This book is in French. The text has two chapters. The first one, written by Waldspurger, proves a twisted version of the local trace formula of Arthur over a local field. This formula is an equality between two expressions, one involving weighted orbital integrals, the other one involving weighted characters. The authors follow Arthur's proof, but the treatement of the spectral side is more complicated in the twisted situation. They need to use the combinatorics of the "Morning Seminar". The authors' local trace formula has the same consequences as in Arthur's paper on elliptic characters. The second chapter, written by Moeglin, gives a symmetric form of the local trace formula as in Arthur's paper on Fourier Transform of Orbital integral and describes any twisted orbital integral, in the p-adic case, as integral of characters.

  17. Spontaneous regression of pulmonary bullae

    International Nuclear Information System (INIS)

    Satoh, H.; Ishikawa, H.; Ohtsuka, M.; Sekizawa, K.

    2002-01-01

    The natural history of pulmonary bullae is often characterized by gradual, progressive enlargement. Spontaneous regression of bullae is, however, very rare. We report a case in which complete resolution of pulmonary bullae in the left upper lung occurred spontaneously. The management of pulmonary bullae is occasionally made difficult because of gradual progressive enlargement associated with abnormal pulmonary function. Some patients have multiple bulla in both lungs and/or have a history of pulmonary emphysema. Others have a giant bulla without emphysematous change in the lungs. Our present case had treated lung cancer with no evidence of local recurrence. He had no emphysematous change in lung function test and had no complaints, although the high resolution CT scan shows evidence of underlying minimal changes of emphysema. Ortin and Gurney presented three cases of spontaneous reduction in size of bulla. Interestingly, one of them had a marked decrease in the size of a bulla in association with thickening of the wall of the bulla, which was observed in our patient. This case we describe is of interest, not only because of the rarity with which regression of pulmonary bulla has been reported in the literature, but also because of the spontaneous improvements in the radiological picture in the absence of overt infection or tumor. Copyright (2002) Blackwell Science Pty Ltd

  18. In vitro anticoagulation monitoring of low-molecular-weight heparin

    Institute of Scientific and Technical Information of China (English)

    WANG Jian-qi; SHI Xu-bo; YANG Jin-gang; HU Da-yi

    2009-01-01

    Background Although low-molecular-weight heparin has replaced unfractionated heparin to become the primary anticoagulation drug for treatment of acute coronary syndrome, there is no convenient bedside monitoring method. We explored the best laboratory monitoring method of low-molecular-weight heparins (enoxapadn, dalteparin, and nadroparin) by use of the Sonoclot coagulation analyzer to monitor the activated clotting time.Methods Atotal of 20 healthy volunteers were selected and 15 ml of fasting venous blood samples were collected and incubated. Four coagulants, kaolin, diatomite, glass bead, and magnetic stick, were used to determine the activated clotting time of the low-molecular-weight heparins at different in vitro anti-Xa factor concentrations. A correlation analysis was made to obtain the regression equation. The activated clotting time of the different low-molecular-weight heparins with the same anti-Xa factor concentration was monitored when the coagulant glass beads were applied. Results The activated clotting time measured using the glass beads, diatomite, kaolin, and magnetic stick showed a linear correlation with the concentration of nadroparin (r= 0.964, 0.966, 0.970, and 0.947, respectively). The regression equation showed that the linear slopes of different coagulants were significantly different (glass beads 230.03 s/IU,diatomite 89.91 s/IU, kaolin 50.87 s/IU, magnetic stick could not be calculated). When the concentration of the anti-Xa factor was the same for different low-molecular-weight heparins, the measured activated clotting time was different after the application of the glass bead coagulant.Conclusions The glass bead coagulant is most feasible for monitoring the in vitro anticoagulation activity of nadroparin.The different effects of different low-molecular-weight heparins on the activated clotting time may be related to the different anti-Ila activities.

  19. Introducing local property tax for fiscal decentralization and local authority autonomy

    Science.gov (United States)

    Dimopoulos, Thomas; Labropoulos, Tassos; Hadjimitsis, Diafantos G.

    2015-06-01

    Charles Tiebout (1956), in his work "A Pure Theory of Local Expenditures", provides a vision of the workings of the local public sector, acknowledging many similarities to the features of a competitive market, however omitting any references to local taxation. Contrary to other researchers' claim that the Tiebout model and the theory of fiscal decentralization are by no means synonymous, this paper aims to expand Tiebout's theory, by adding the local property tax in the context, introducing a fair, ad valorem property taxation system based on the automated assessment of the value of real estate properties within the boundaries of local authorities. Computer Assisted Mass Appraisal methodology integrated with Remote Sensing technology and GIS analysis is applied to local authorities' property registries and cadastral data, building a spatial relational database and providing data to be statistically processed through Multiple Regression Analysis modeling. The proposed scheme accomplishes economy of scale using CAMA procedures on one hand, but also succeeds in making local authorities self-sufficient through a decentralized, fair, locally calibrated property taxation model, providing rational income administration.

  20. Local concentration of fast-food outlets is associated with poor nutrition and obesity.

    Science.gov (United States)

    Kruger, Daniel J; Greenberg, Emily; Murphy, Jillian B; DiFazio, Lindsay A; Youra, Kathryn R

    2014-01-01

    We investigated the relationship of the local availability of fast-food restaurant locations with diet and obesity. We geocoded addresses of survey respondents and fast-food restaurant locations to assess the association between the local concentration of fast-food outlets, BMI, and fruit and vegetable consumption. The survey was conducted in Genesee County, Michigan. There were 1345 individuals included in this analysis, and the response rate was 25%. The Speak to Your Health! Community Survey included fruit and vegetable consumption items from the Behavioral Risk Factor Surveillance System, height, weight, and demographics. We used ArcGIS to map fast-food outlets and survey respondents. Stepwise linear regressions identified unique predictors of body mass index (BMI) and fruit and vegetable consumption. Survey respondents had 8 ± 7 fast-food outlets within 2 miles of their home. Individuals living in close proximity to fast-food restaurants had higher BMIs t(1342) = 3.21, p food availability, which may constrain the success of nutrition promotion efforts. Efforts to decrease the local availability of unhealthy foods as well as programs to help consumers identify strategies for obtaining healthy meals at fast-food outlets may improve health outcomes.

  1. Tensor-based cortical surface morphometry via weighted spherical harmonic representation.

    Science.gov (United States)

    Chung, Moo K; Dalton, Kim M; Davidson, Richard J

    2008-08-01

    We present a new tensor-based morphometric framework that quantifies cortical shape variations using a local area element. The local area element is computed from the Riemannian metric tensors, which are obtained from the smooth functional parametrization of a cortical mesh. For the smooth parametrization, we have developed a novel weighted spherical harmonic (SPHARM) representation, which generalizes the traditional SPHARM as a special case. For a specific choice of weights, the weighted-SPHARM is shown to be the least squares approximation to the solution of an isotropic heat diffusion on a unit sphere. The main aims of this paper are to present the weighted-SPHARM and to show how it can be used in the tensor-based morphometry. As an illustration, the methodology has been applied in the problem of detecting abnormal cortical regions in the group of high functioning autistic subjects.

  2. A regression approach for zircaloy-2 in-reactor creep constitutive equations

    International Nuclear Information System (INIS)

    Yung Liu, Y.; Bement, A.L.

    1977-01-01

    In this paper the methodology of multiple regressions as applied to zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. From data analysis and model development point of views, both the assumption of independence and prior committment to specific model forms are unacceptable. One would desire means which can not only estimate the required parameters directly from data but also provide basis for model selections, viz., one model against others. Basic understanding of the physics of deformation is important in choosing the forms of starting physical model equations, but the justifications must rely on their abilities in correlating the overall data. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) when there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets, (2) regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections

  3. Metabolic Benefits of Prior Weight Loss with and without Exercise on Subsequent 6-Month Weight Regain.

    Science.gov (United States)

    Ryan, Alice S; Serra, Monica C; Goldberg, Andrew P

    2018-01-01

    To determine the 6-month follow-up effects after intentional 6-month weight loss alone (WL) and after weight loss with aerobic exercise (AEX + WL) on body composition, glucose metabolism, and cardiovascular disease risk factors in older postmenopausal women and to identify the mechanisms for weight regain. Women (n = 65, BMI > 25 kg/m 2 ) underwent maximal oxygen consumption testing, dual-energy x-ray absorptiometry, computed tomography scans, and oral glucose tolerance tests before and after 6 months of AEX + WL or WL and at 12 months ad libitum follow-up. Insulin sensitivity (M) (hyperinsulinemic-euglycemic clamp) was measured at baseline and 6 months. Thirty WL and thirty-five AEX + WL women completed a follow-up at 12 months. Similar weight loss was observed (-8%) in both groups from 0 to 6 months. Total fat mass, fat-free mass, visceral fat area, subcutaneous abdominal and midthigh fat areas, fasting glucose, insulin levels, homeostatic model assessment of insulin resistance (HOMA-IR), insulin areas under the curve, and triglyceride levels decreased similarly after WL and AEX + WL and remained lower at 12 months than at baseline, despite weight regain at 12 months. Initial M was associated with weight regain (r = -0.40, P < 0.01). Weight regain was related to independent changes in leptin and HOMA-IR from 6 to 12 months in a multiple regression model (r = 0.77, P < 0.0001). Reductions in body fat and improvements in insulin sensitivity after AEX + WL and WL were maintained at 12 months despite modest weight regain. Baseline insulin resistance partially predicted the magnitude of weight regain in postmenopausal women. © 2017 The Obesity Society.

  4. Sociocultural and Familial Factors Associated with Weight Bias Internalization

    Directory of Open Access Journals (Sweden)

    Rebecca L. Pearl

    2018-04-01

    Full Text Available Background/Aims: Sociocultural and familial factors associated with weight bias internalization (WBI are currently unknown. The present study explored the relationship between interpersonal sources of weight stigma, family weight history, and WBI. Methods: Participants with obesity (N = 178, 87.6% female, 71.3% black completed questionnaires that assessed the frequency with which they experienced weight stigma from various interpersonal sources. Participants also reported the weight status of their family members and completed measures of WBI, depression, and demographics. Participant height and weight were measured to calculate body mass index (BMI. Results: Linear regression results (controlling for demographics, BMI, and depression showed that stigmatizing experiences from family and work predicted greater WBI. Experiencing weight stigma at work was associated with WBI above and beyond the effects of other sources of stigma. Participants who reported higher BMIs for their mothers had lower levels of WBI. Conclusion: Experiencing weight stigma from family and at work may heighten WBI, while having a mother with a higher BMI may be a protective factor against WBI. Prospective research is needed to understand WBI's developmental course and identify mechanisms that increase or mitigate its risk.

  5. Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models

    Directory of Open Access Journals (Sweden)

    Seyed Mehran Kazemi

    2018-02-01

    Full Text Available The aim of statistical relational learning is to learn statistical models from relational or graph-structured data. Three main statistical relational learning paradigms include weighted rule learning, random walks on graphs, and tensor factorization. These paradigms have been mostly developed and studied in isolation for many years, with few works attempting at understanding the relationship among them or combining them. In this article, we study the relationship between the path ranking algorithm (PRA, one of the most well-known relational learning methods in the graph random walk paradigm, and relational logistic regression (RLR, one of the recent developments in weighted rule learning. We provide a simple way to normalize relations and prove that relational logistic regression using normalized relations generalizes the path ranking algorithm. This result provides a better understanding of relational learning, especially for the weighted rule learning and graph random walk paradigms. It opens up the possibility of using the more flexible RLR rules within PRA models and even generalizing both by including normalized and unnormalized relations in the same model.

  6. Pre-Pregnancy Body Mass Index, Gestational Weight Gain, and Birth Weight: A Cohort Study in China.

    Directory of Open Access Journals (Sweden)

    Shaoping Yang

    Full Text Available To assess whether pre-pregnancy body mass index (BMI modify the relationship between gestational weight gain (GWG and child birth weight (specifically, presence or absence of low birth weight (LBW or presence of absence of macrosomia, and estimates of the relative risk of macrosomia and LBW based on pre-pregnancy BMI were controlled in Wuhan, China.From June 30, 2011 to June 30, 2013. All data was collected and available from the perinatal health care system. Logistic regression models were used to estimate the independent association among pregnancy weight gain, LBW, normal birth weight, and macrosomia within different pre-pregnancy BMI groups. We built different logistic models for the 2009 Institute of Medicine (IOM Guidelines and Chinese-recommended GWG which was made from this sample. The Chinese-recommended GWG was derived from the quartile values (25th-75th percentiles of weight gain at the time of delivery in the subjects which comprised our sample.For LBW children, using the recommended weight gain of the IOM and Chinese women as a reference, the OR for a pregnancy weight gain below recommendations resulted in a positive relationship for lean and normal weight women, but not for overweight and obese women. For macrosomia, considering the IOM's recommended weight gain as a reference, the OR magnitude for pregnancy weight gain above recommendations resulted in a positive correlation for all women. The OR for a pregnancy weight gain below recommendations resulted in a negative relationship for normal BMI and lean women, but not for overweight and obese women based on the IOM recommendations, significant based on the recommended pregnancy weight gain for Chinese women. Of normal weight children, 56.6% were above the GWG based on IOM recommendations, but 26.97% of normal weight children were above the GWG based on Chinese recommendations.A GWG above IOM recommendations might not be helpful for Chinese women. We need unified criteria to

  7. Customized Fetal Growth Charts for Parents' Characteristics, Race, and Parity by Quantile Regression Analysis: A Cross-sectional Multicenter Italian Study.

    Science.gov (United States)

    Ghi, Tullio; Cariello, Luisa; Rizzo, Ludovica; Ferrazzi, Enrico; Periti, Enrico; Prefumo, Federico; Stampalija, Tamara; Viora, Elsa; Verrotti, Carla; Rizzo, Giuseppe

    2016-01-01

    The purpose of this study was to construct fetal biometric charts between 16 and 40 weeks' gestation that were customized for parental characteristics, race, and parity, using quantile regression analysis. In a multicenter cross-sectional study, 8070 sonographic examinations from low-risk pregnancies between 16 and 40 weeks' gestation were analyzed. The fetal measurements obtained were biparietal diameter, head circumference, abdominal circumference, and femur diaphysis length. Quantile regression was used to examine the impact of parental height and weight, parity, and race across biometric percentiles for the fetal measurements considered. Paternal and maternal height were significant covariates for all of the measurements considered (P < .05). Maternal weight significantly influenced head circumference, abdominal circumference, and femur diaphysis length. Parity was significantly associated with biparietal diameter and head circumference. Central African race was associated with head circumference and femur diaphysis length, whereas North African race was only associated with femur diaphysis length. In this study we constructed customized biometric growth charts using quantile regression in a large cohort of low-risk pregnancies. These charts offer the advantage of defining individualized normal ranges of fetal biometric parameters at each specific percentile corrected for parental height and weight, parity, and race. This study supports the importance of including these variables in routine sonographic screening for fetal growth abnormalities.

  8. Time-adaptive quantile regression

    DEFF Research Database (Denmark)

    Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik

    2008-01-01

    and an updating procedure are combined into a new algorithm for time-adaptive quantile regression, which generates new solutions on the basis of the old solution, leading to savings in computation time. The suggested algorithm is tested against a static quantile regression model on a data set with wind power......An algorithm for time-adaptive quantile regression is presented. The algorithm is based on the simplex algorithm, and the linear optimization formulation of the quantile regression problem is given. The observations have been split to allow a direct use of the simplex algorithm. The simplex method...... production, where the models combine splines and quantile regression. The comparison indicates superior performance for the time-adaptive quantile regression in all the performance parameters considered....

  9. An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

    Science.gov (United States)

    Chardon, Jérémy; Hingray, Benoit; Favre, Anne-Catherine

    2018-01-01

    Statistical downscaling models (SDMs) are often used to produce local weather scenarios from large-scale atmospheric information. SDMs include transfer functions which are based on a statistical link identified from observations between local weather and a set of large-scale predictors. As physical processes driving surface weather vary in time, the most relevant predictors and the regression link are likely to vary in time too. This is well known for precipitation for instance and the link is thus often estimated after some seasonal stratification of the data. In this study, we present a two-stage analog/regression model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are identified from fields of geopotential heights at 1000 and 500 hPa. For the regression stage, two generalized linear models are further used to model the probability of precipitation occurrence and the distribution of non-zero precipitation amounts, respectively. The two-stage model is evaluated for the probabilistic prediction of small-scale precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and amount. As the analog days vary from one prediction day to another, the atmospheric predictors selected in the regression stage and the value of the corresponding regression coefficients can vary from one prediction day to another. The model allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations.

  10. Mass movement susceptibility mapping - A comparison of logistic regression and Weight of evidence methods in Taounate-Ain Aicha region (Central Rif, Morocco

    Directory of Open Access Journals (Sweden)

    JEMMAH A I

    2018-01-01

    Full Text Available Taounate region is known by a high density of mass movements which cause several human and economic losses. The goal of this paper is to assess the landslide susceptibility of Taounate using the Weight of Evidence method (WofE and the Logistic Regression method (LR. Seven conditioning factors were used in this study: lithology, fault, drainage, slope, elevation, exposure and land use. Over the years, this site and its surroundings have experienced repeated landslides. For this reason, landslide susceptibility mapping is mandatory for risk prevention and land-use management. In this study, we have focused on recent large-scale mass movements. Finally, the ROC curves were established to evaluate the degree of fit of the model and to choose the best landslide susceptibility zonation. A total mass movements location were detected; 50% were randomly selected as input data for the entire process using the Spatial Data Model (SDM and the remaining locations were used for validation purposes. The obtained WofE’s landslide susceptibility map shows that high to very high susceptibility zones contain 62% of the total of inventoried landslides, while the same zones contain only 47% of landslides in the map obtained by the LR method. This landslide susceptibility map obtained is a major contribution to various urban and regional development plans under the Taounate Region National Development Program.

  11. Regression analysis: An evaluation of the inuences behindthe pricing of beer

    OpenAIRE

    Eriksson, Sara; Häggmark, Jonas

    2017-01-01

    This bachelor thesis in applied mathematics is an analysis of which factors affect the pricing of beer at the Swedish market. A multiple linear regression model is created with the statistical programming language R through a study of the influences for several explanatory variables. For example these variables include country of origin, beer style, volume sold and a Bayesian weighted mean rating from RateBeer, a popular website for beer enthusiasts. The main goal of the project is to find si...

  12. Influence of gestational weight gain on low birth weight in short-statured South Indian pregnant women.

    Science.gov (United States)

    Shivakumar, Nirupama; Dwarkanath, Pratibha; Bosch, Ronald; Duggan, Christopher; Kurpad, Anura V; Thomas, Tinku

    2018-05-01

    India contributes to one-third of the global burden of low birth weight (LBW) neonates, which is associated with increased risk of mortality and adverse consequences on long-term health. Factors leading to LBW are multidimensional and maternal short stature is an important component with an inter-generational effect. On the contrary gestational weight gain (GWG) shows an independent positive influence on birth weight. The aim of the present study therefore was to determine the influence of GWG on birth weight in short pregnant women. A prospective observational cohort of 1254 pregnant women was studied. Total, second and third trimester GWG per week were computed. Women were divided into two groups, "short" and "not-short", using a cut off of 152 cm that corresponded to the 25th percentile for height in the cohort. Association of tertiles of GWG with LBW was examined using log binomial regression analysis. "Short" women in highest tertile of total GWG had a significantly reduced adjusted relative risk (ARR 0.37, 95% confidence interval 0.16-0.83, P = 0.016) for LBW, compared to the lowest tertile. However, there was no significant increase in risk for cesarean section (CS) with increasing tertiles of total GWG. In women with height women may be beneficial for the birth weight of the offspring.

  13. Weight management behaviors in a sample of Iranian adolescent girls.

    Science.gov (United States)

    Garousi, S; Garrusi, B; Baneshi, Mohammad Reza; Sharifi, Z

    2016-09-01

    Attempts to obtain the ideal body shape portrayed in advertising can result in behaviors that lead to an unhealthy reduction in weight. This study was designed to identify contributing factors that may be effective in changing the behavior of a sample of Iranian adolescents. Three hundred fifty adolescent girls from high schools in Kerman, Iran participated in a cross-sectional study based on a self-administered questionnaire. Multifactorial logistic regression modeling was used to identify the factors influencing each of the contributing factors for body management methods, and a decision tree model was constructed to identify individuals who were more or less likely to change their body shape. Approximately one-third of the adolescent girls had attempted dieting, and 37 % of them had exercised to lose weight. The logistic regression model showed that pressure from their mother and the media; father's education level; and body mass index (BMI) were important factors in dieting. BMI and perceived pressure from the media were risk factors for attempting exercise. BMI and perceived pressure from relatives, particularly mothers, and the media were important factors in attempts by adolescent girls to lose weight.

  14. STOCK Market Differences in Correlation-Based Weighted Network

    Science.gov (United States)

    Youn, Janghyuk; Lee, Junghoon; Chang, Woojin

    We examined the sector dynamics of Korean stock market in relation to the market volatility. The daily price data of 360 stocks for 5019 trading days (from January, 1990 to August, 2008) in Korean stock market are used. We performed the weighted network analysis and employed four measures: the average, the variance, the intensity, and the coherence of network weights (absolute values of stock return correlations) to investigate the network structure of Korean stock market. We performed regression analysis using the four measures in the seven major industry sectors and the market (seven sectors combined). We found that the average, the intensity, and the coherence of sector (subnetwork) weights increase as market becomes volatile. Except for the "Financials" sector, the variance of sector weights also grows as market volatility increases. Based on the four measures, we can categorize "Financials," "Information Technology" and "Industrials" sectors into one group, and "Materials" and "Consumer Discretionary" sectors into another group. We investigated the distributions of intrasector and intersector weights for each sector and found the differences in "Financials" sector are most distinct.

  15. FATAL, General Experiment Fitting Program by Nonlinear Regression Method

    International Nuclear Information System (INIS)

    Salmon, L.; Budd, T.; Marshall, M.

    1982-01-01

    1 - Description of problem or function: A generalized fitting program with a free-format keyword interface to the user. It permits experimental data to be fitted by non-linear regression methods to any function describable by the user. The user requires the minimum of computer experience but needs to provide a subroutine to define his function. Some statistical output is included as well as 'best' estimates of the function's parameters. 2 - Method of solution: The regression method used is based on a minimization technique devised by Powell (Harwell Subroutine Library VA05A, 1972) which does not require the use of analytical derivatives. The method employs a quasi-Newton procedure balanced with a steepest descent correction. Experience shows this to be efficient for a very wide range of application. 3 - Restrictions on the complexity of the problem: The current version of the program permits functions to be defined with up to 20 parameters. The function may be fitted to a maximum of 400 points, preferably with estimated values of weight given

  16. A Prospective Study of Depression and Weight Change After Kidney Transplant.

    Science.gov (United States)

    Stanfill, Ansley; Hathaway, Donna; Bloodworth, Robin; Cashion, Ann

    2016-03-01

    Kidney transplant recipients have great risk for gaining significant weight (upward of 10 kg) in the first year posttransplant. Clinical depression can occur in response to life situations and is associated with weight gain. To explore the association between demographic characteristics, weight change, and depression posttransplantation. Secondary data analysis on longitudinal data collected for a larger observational study. Demographic characteristics, weight, and Center for Epidemiologic Studies Depression Scale (CES-D) data were obtained at baseline (BL) (time of transplantation), 6, and 12 months posttransplant. The CES-D scores were compared among time points using means, standard deviations, correlations, t tests, and chi-square as well as by multiple regression modeling. Regional transplant center in the mid-south United States. Forty-seven kidney transplant recipients (55% female, 57% African American, mean age 52.5 years). Weight change ranged from -18.1 to +24.8 kg. In all, 62% reported baseline CES-D scores indicative of depression, with lower scores indicating less psychological distress at 6 and 12 months (47% and 49%, respectively). We found no significant differences among CES-D scores at any time point. Regression models found age, race, gender, and weight change to be predictive of CES-D scores at 6 months (P = .04, R (2) = .137). Age was the most influential (P = .008), with older individuals more likely to obtain higher CES-D scores. Since the experience of depression is common at transplant and during the first year, it is important that transplant recipients be evaluated for depression early in the recovery period. © 2016, NATCO.

  17. Overvaluation of shape and weight in adolescents with anorexia nervosa: does shape concern or weight concern matter more for treatment outcome?

    Science.gov (United States)

    Byrne, Catherine E; Kass, Andrea E; Accurso, Erin C; Fischer, Sarah; O'Brien, Setareh; Goodyear, Alexandria; Lock, James; Le Grange, Daniel

    2015-01-01

    Overvaluation of shape and weight is a key diagnostic feature of anorexia nervosa (AN); however, limited research has evaluated the clinical utility of differentiating between weight versus shape concerns. Understanding differences in these constructs may have important implications for AN treatment given the focus on weight regain. This study examined differences in treatment outcome between individuals whose primary concern was weight versus those whose primary concern was shape in a randomized controlled trial of treatment for adolescent AN. Data were drawn from a two-site randomized controlled trial that compared family-based treatment and adolescent focused therapy for AN. Chi-square tests and logistic regression analyses were conducted. Thirty percent of participants presented with primary weight concern (n = 36; defined as endorsing higher Eating Disorder Examination (EDE) Weight Concern than Shape Concern subscale scores); 60 % presented with primary shape concern (n = 72; defined as endorsing higher EDE Shape Concern than Weight Concern scores). There were no significant differences between the two groups in remission status at the end of treatment. Treatment did not moderate the effect of group status on achieving remission. Results suggest that treatment outcomes are comparable between adolescents who enter treatment for AN with greater weight concerns and those who enter treatment with greater shape concerns. Therefore, treatment need not be adjusted based on primary weight or primary shape concerns.

  18. Associations of neighbourhood walkability indices with weight gain.

    Science.gov (United States)

    Koohsari, Mohammad Javad; Oka, Koichiro; Shibata, Ai; Liao, Yung; Hanibuchi, Tomoya; Owen, Neville; Sugiyama, Takemi

    2018-04-03

    Inconsistent associations of neighbourhood walkability with adults' body weight have been reported. Most studies examining the relationships of walkability and adiposity are cross-sectional in design. We examined the longitudinal relationships of two walkability indices - conventional walkability and space syntax walkability, and their individual components, with weight change among adults over four years. Data were from the Physical Activity in Localities and Community study in Adelaide, Australia. In 2003-2004, 2650 adults living in 154 Census Collection Districts (CCDs) returned baseline questionnaires; in 2007-2008, the follow-up survey was completed by 1098. Participants reported their weight at baseline and at follow-up. Neighbourhood walkability indices were calculated using geographic information systems and space syntax software. Linear marginal models using generalized estimating equations with robust standard errors were fitted to examine associations of the two walkability indices and their individual components with the weight at follow-up, adjusting for baseline weight, socio-demographic variables, and spatial clustering at the level of CCD. The overall mean weight gain over four years was 1.5 kg. The two walkability indices were closely correlated (r = 0.76, p walkability indices and weight change. Among walkability components, there was a marginally significant negative association between space syntax measure of street integration and weight change: one standard deviation increment in street integration was associated with 0.31 kg less weight gain (p = 0.09). Using a prospective study design and a novel space-syntax based measure of walkability, we were not able to identify relationships between neighbourhood walkability with weight gain. This is consistent with other inconclusive findings on the built environment and obesity. Research on the built environment and adults' weight gain may need to consider not just local environments but

  19. Neighborhood racial composition and poverty in association with pre-pregnancy weight and gestational weight gain

    Directory of Open Access Journals (Sweden)

    Dara D. Mendez

    2016-12-01

    Full Text Available Background: Studies of neighborhood racial composition or neighborhood poverty in association with pregnancy-related weight are limited. Prior studies of neighborhood racial density and poverty has been in association with adverse birth outcomes and suggest that neighborhoods with high rates of poverty and racial composition of black residents are typically segregated and systematically isolated from opportunities and resources. These neighborhood factors may help explain the racial disparities in pre-pregnancy weight and inadequate weight gain. This study examined whether neighborhood racial composition and neighborhood poverty was associated with weight before pregnancy and weight gain during pregnancy and if this association differed by race. Methods: We used vital birth records of singleton births of 73,061 non-Hispanic black and white women in Allegheny County, PA (2003–2010. Maternal race and ethnicity, pre-pregnancy body-mass-index (BMI, gestational weight gain and other individual-level characteristics were derived from vital birth record data, and measures of neighborhood racial composition (percentage of black residents in the neighborhood and poverty (percentage of households in the neighborhood below the federal poverty were derived using US Census data. Multilevel log binomial regression models were performed to estimate neighborhood racial composition and poverty in association with pre-pregnancy weight (i.e., overweight/obese and gestational weight gain (i.e., inadequate and excessive. Results: Black women as compared to white women were more likely to be overweight/obese before pregnancy and to have inadequate gestational weight gain (53.6% vs. 38.8%; 22.5% vs. 14.75 respectively. Black women living in predominately black neighborhoods were slightly more likely to be obese prior to pregnancy compared to black women living in predominately white neighborhoods (PR 1.10; 95% CI: 1.03, 1.16. Black and white women living in high

  20. Excessive weight loss in exclusively breastfed full-term newborns in a Baby-Friendly Hospital

    Directory of Open Access Journals (Sweden)

    Maria Aparecida Mezzacappa

    Full Text Available Abstract Objective: To determine the risk factors for weight loss over 8% in full-term newborns at postpartum discharge from a Baby Friendly Hospital. Methods: The cases were selected from a cohort of infants belonging to a previous study. Healthy full-term newborns with birth weight ≥2.000g, who were exclusively breastfed were included and excluded twins and those undergoing phototherapy as well as those discharged after 96h of life. The analyzed maternal and neonatal variables were maternal age, parity, ethnicity, type of delivery, maternal diabetes, gender, gestational age and appropriate weight for age. Adjusted multiple and univariate Cox regression analyses were used, considering as significant p8% were cesarean delivery and older maternal age. At the adjusted multiple regression analysis, the model to explain the weight loss was cesarean delivery (Relative risk 2.27, 95% of Confidence Interval 1.54–3.35. Conclusions: The independent predictor for weight loss>8% in exclusively breastfed full-term newborns in a Baby-Friendly Hospital was the cesarean delivery. It is possible to reduce the number of cesarean sections to minimize neonatal excessive weight loss and the resulting use of infant formula during the first week of life.

  1. Radioguided occult lesion localization versus wire-guided localization for non-palpable breast lesions: randomized controlled trial

    International Nuclear Information System (INIS)

    Ocal, Koray; Dag, Ahmet; Turkmenoglu, Ozgur; Yucel, Erdem; Gunay, Emel Ceylan; Duce, Meltem Nass

    2011-01-01

    Aim: this prospective randomized clinical study was conducted to compare radioguided occult lesion localization (ROLL) with wire-guided localization to evaluate optimum localization techniques for non-palpable breast lesions. Methods: a total of 108 patients who were undergoing an excisional biopsy for non-palpable breast lesions requiring pathologic diagnosis were randomly assigned to the ROLL group (n 56) and wire-guided localization group (n 52). In the study, patients' characteristics, radiological abnormalities, radiological technique of localization, localization time, operation time, weight of the excised specimen, clearance margins, pathological diagnosis and perioperative complications were assessed. Results: there were no differences between the two groups in terms of age, radiological abnormalities and localization technique (p = non-significant for all). ROLL techniques resulted in 100% retrieval of the lesions; for the wire-guided localization technique, 98%. Both localization time and operation time were significantly reduced with the ROLL technique (p = significant for all). The weight of the specimen was significantly lower in the ROLL group than in the wire-guided localization group (p = significant). The overall complication rate and pathological diagnosis were similar for both groups (p = non-significant for all). Clear margins were achieved in 91% of ROLL patients and in 53% of wire-guided localization patients, and the difference was significant. Conclusions: the present study indicated that the ROLL technique is as effective as wire-guided localization for the excision of non-palpable breast lesions. In addition, ROLL improved the outcomes by reducing localization and operation time, preventing healthy tissue excision and achieving clearer margins. (author)

  2. Radioguided occult lesion localization versus wire-guided localization for non-palpable breast lesions: randomized controlled trial

    Energy Technology Data Exchange (ETDEWEB)

    Ocal, Koray; Dag, Ahmet; Turkmenoglu, Ozgur; Yucel, Erdem [Mersin University (Turkey). Medical Faculty. Dept. of General Surgery; Gunay, Emel Ceylan [Mersin University (Turkey). Medical Faculty. Dept. of Nuclear Medicine; Duce, Meltem Nass [Mersin University (Turkey). Medical Faculty. Dept. of Radiology

    2011-07-01

    Aim: this prospective randomized clinical study was conducted to compare radioguided occult lesion localization (ROLL) with wire-guided localization to evaluate optimum localization techniques for non-palpable breast lesions. Methods: a total of 108 patients who were undergoing an excisional biopsy for non-palpable breast lesions requiring pathologic diagnosis were randomly assigned to the ROLL group (n 56) and wire-guided localization group (n 52). In the study, patients' characteristics, radiological abnormalities, radiological technique of localization, localization time, operation time, weight of the excised specimen, clearance margins, pathological diagnosis and perioperative complications were assessed. Results: there were no differences between the two groups in terms of age, radiological abnormalities and localization technique (p = non-significant for all). ROLL techniques resulted in 100% retrieval of the lesions; for the wire-guided localization technique, 98%. Both localization time and operation time were significantly reduced with the ROLL technique (p = significant for all). The weight of the specimen was significantly lower in the ROLL group than in the wire-guided localization group (p = significant). The overall complication rate and pathological diagnosis were similar for both groups (p = non-significant for all). Clear margins were achieved in 91% of ROLL patients and in 53% of wire-guided localization patients, and the difference was significant. Conclusions: the present study indicated that the ROLL technique is as effective as wire-guided localization for the excision of non-palpable breast lesions. In addition, ROLL improved the outcomes by reducing localization and operation time, preventing healthy tissue excision and achieving clearer margins. (author)

  3. Approximate median regression for complex survey data with skewed response.

    Science.gov (United States)

    Fraser, Raphael André; Lipsitz, Stuart R; Sinha, Debajyoti; Fitzmaurice, Garrett M; Pan, Yi

    2016-12-01

    The ready availability of public-use data from various large national complex surveys has immense potential for the assessment of population characteristics using regression models. Complex surveys can be used to identify risk factors for important diseases such as cancer. Existing statistical methods based on estimating equations and/or utilizing resampling methods are often not valid with survey data due to complex survey design features. That is, stratification, multistage sampling, and weighting. In this article, we accommodate these design features in the analysis of highly skewed response variables arising from large complex surveys. Specifically, we propose a double-transform-both-sides (DTBS)'based estimating equations approach to estimate the median regression parameters of the highly skewed response; the DTBS approach applies the same Box-Cox type transformation twice to both the outcome and regression function. The usual sandwich variance estimate can be used in our approach, whereas a resampling approach would be needed for a pseudo-likelihood based on minimizing absolute deviations (MAD). Furthermore, the approach is relatively robust to the true underlying distribution, and has much smaller mean square error than a MAD approach. The method is motivated by an analysis of laboratory data on urinary iodine (UI) concentration from the National Health and Nutrition Examination Survey. © 2016, The International Biometric Society.

  4. Mobile Visual Search Based on Histogram Matching and Zone Weight Learning

    Science.gov (United States)

    Zhu, Chuang; Tao, Li; Yang, Fan; Lu, Tao; Jia, Huizhu; Xie, Xiaodong

    2018-01-01

    In this paper, we propose a novel image retrieval algorithm for mobile visual search. At first, a short visual codebook is generated based on the descriptor database to represent the statistical information of the dataset. Then, an accurate local descriptor similarity score is computed by merging the tf-idf weighted histogram matching and the weighting strategy in compact descriptors for visual search (CDVS). At last, both the global descriptor matching score and the local descriptor similarity score are summed up to rerank the retrieval results according to the learned zone weights. The results show that the proposed approach outperforms the state-of-the-art image retrieval method in CDVS.

  5. The Impact of Impulsivity on Weight Loss Four Years after Bariatric Surgery

    Directory of Open Access Journals (Sweden)

    Kathrin Schag

    2016-11-01

    Full Text Available Bariatric surgery has serious implications on metabolic health. The reasons for a failure of bariatric surgery, i.e., limited weight loss, are multifactorial and include psychological factors. We established a theoretical model of how impulsivity is related to weight loss outcome. We propose that depressive symptoms act as a mediator between impulsivity and pathological eating behavior, and that pathological eating behavior has a direct impact on weight loss outcome. We calculated excessive weight loss (%EWL and assessed self-reported impulsivity (using the Baratt Impulsiveness Scale (BIS-15 total score, depressive symptoms (the Patient Health Questionnaire (PHQ-9 score, and pathological eating behavior (the Eating Disorder Inventory 2 (EDI-2 total score in 65 patients four years after laparoscopic sleeve gastrectomy. Regression and mediation analyses were computed to validate the theoretical model. The BIS-15, PHQ-9, and EDI-2 have medium to high correlations between each other, and EDI-2 correlated with %EWL. The mediation analysis yielded that the PHQ-9 represents a significant mediator between BIS-15 and EDI-2. The regression model between EDI-2 and %EWL was also significant. These results support our theoretical model, i.e., suggest that impulsivity has an indirect impact on weight loss outcome after bariatric surgery, mediated by depression and transferred through pathological eating behavior. Thus, the underlying psychological factors should be addressed in post-operative care to optimize weight loss outcome.

  6. Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR is an efficient tool for metamodelling of nonlinear dynamic models

    Directory of Open Access Journals (Sweden)

    Omholt Stig W

    2011-06-01

    Full Text Available Abstract Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs to variation in features of the trajectories of the state variables (outputs throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR, where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR and ordinary least squares (OLS regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback

  7. Hierarchical cluster-based partial least squares regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models.

    Science.gov (United States)

    Tøndel, Kristin; Indahl, Ulf G; Gjuvsland, Arne B; Vik, Jon Olav; Hunter, Peter; Omholt, Stig W; Martens, Harald

    2011-06-01

    Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. HC-PLSR is a promising approach for

  8. Sonographic fetal weight estimation using femoral length: Honarvar Equation

    International Nuclear Information System (INIS)

    Firoozabadi, Raziah Dehghani; Ghasemi, N.; Firoozabadi, Mehdi Dehghani

    2007-01-01

    Fetal growth is the result of interactions between various factors and can be estimated by ultrasonic measurements. Fetal femur length is a scale for estimating the fetal weight in individual races because fetal growth patterns differ among different races. This was a prospective study involving 500 pregnant women at 36 weeks of gestational age. Real-time sonography was done to measure the femoral length and the weight of the fetus was estimated by the Honarvar 2 equation. The correlation between estimated fetal weight (EFW) and real weight was tested by Pearson correlation coefficient and relationships with the age and BMI of mother, the sex of the neonate and parity were tested by multiple regression. EFW by the Honarvar 2 equation correlated significantly with actual birthweight. Therefore, this equation is valid for fetal weight estimation. It also does not depend on the age and BMI of the mother, sex of the neonate, parity. Ethnicity potentially plays an important role in the fetal weight estimation. The Honarvar formula produced the best estimate of the actual birthweight for Iranian fetuses, and its use is recommended. (author)

  9. Longitudinal drop-out and weighting against its bias

    Directory of Open Access Journals (Sweden)

    Steffen C. E. Schmidt

    2017-12-01

    Full Text Available Abstract Background The bias caused by drop-out is an important factor in large population-based epidemiological studies. Many studies account for it by weighting their longitudinal data, but to date there is no detailed final approach for how to conduct these weights. Methods In this study we describe the observed longitudinal bias and a three-step longitudinal weighting approach used for the longitudinal data in the MoMo baseline (N = 4528, 4–17 years and wave 1 study with 2807 (62% participants between 2003 and 2012. Results The most meaningful drop-out predictors were socioeconomic status of the household, socioeconomic characteristics of the mother and daily TV usage. Weighting reduced the bias between the longitudinal participants and the baseline sample, and also increased variance by 5% to 35% with a final weighting efficiency of 41.67%. Conclusions We conclude that a weighting procedure is important to reduce longitudinal bias in health-oriented epidemiological studies and suggest identifying the most influencing variables in the first step, then use logistic regression modeling to calculate the inverse of the probability of participation in the second step, and finally trim and standardize the weights in the third step.

  10. Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter

    Directory of Open Access Journals (Sweden)

    Álvaro Moreno

    2014-08-01

    Full Text Available Time series of remotely sensed data are an important source of information for understanding land cover dynamics. In particular, the fraction of absorbed photosynthetic active radiation (fAPAR is a key variable in the assessment of vegetation primary production over time. However, the fAPAR series derived from polar orbit satellites are not continuous and consistent in space and time. Filtering methods are thus required to fill in gaps and produce high-quality time series. This study proposes an adapted (iteratively reweighted local regression filter (LOESS and performs a benchmarking intercomparison with four popular and generally applicable smoothing methods: Double Logistic (DLOG, smoothing spline (SSP, Interpolation for Data Reconstruction (IDR and adaptive Savitzky-Golay (ASG. This paper evaluates the main advantages and drawbacks of the considered techniques. The results have shown that ASG and the adapted LOESS perform better in recovering fAPAR time series over multiple controlled noisy scenarios. Both methods can robustly reconstruct the fAPAR trajectories, reducing the noise up to 80% in the worst simulation scenario, which might be attributed to the quality control (QC MODIS information incorporated into these filtering algorithms, their flexibility and adaptation to the upper envelope. The adapted LOESS is particularly resistant to outliers. This method clearly outperforms the other considered methods to deal with the high presence of gaps and noise in satellite data records. The low RMSE and biases obtained with the LOESS method (|rMBE| < 8%; rRMSE < 20% reveals an optimal reconstruction even in most extreme situations with long seasonal gaps. An example of application of the LOESS method to fill in invalid values in real MODIS images presenting persistent cloud and snow coverage is also shown. The LOESS approach is recommended in most remote sensing applications, such as gap-filling, cloud-replacement, and observing temporal

  11. Parent and family associations with weight-related behaviors and cognitions among overweight adolescents

    Science.gov (United States)

    Cromley, Taya R.; Neumark-Sztainer, Dianne; Story, Mary; Boutelle, Kerri

    2013-01-01

    Purpose To examine parent and family variables in relation to adolescent weight control and eating behaviors, body satisfaction, and importance of thinness among overweight adolescents. Methods This study examined parent-reported use of weight control behaviors (i.e., healthy and unhealthy behaviors, behavioral changes, other diet strategies), parent psychosocial functioning (i.e., depression, self-esteem, body satisfaction, importance of thinness), and family functioning (i.e., cohesion and adaptability) in relation to adolescent weight control and eating behaviors, body satisfaction, and importance of thinness. Surveys were completed by 103 overweight (BMI ≥ 85th percentile) adolescents, ages 12 to 20, and their parents. Height and weight were also measured. Linear regression equations were used for continuous outcomes and logistic regression equations for dichotomous outcomes. Results Adolescent report of lower body satisfaction and engagement in more “severe” or less healthy forms of weight control behavior were associated with parent weight control behaviors. Adolescent report of overeating was associated with lower scores of family cohesion and adaptability. Adolescent report of lower body satisfaction was positively associated with parent report of body satisfaction and self-esteem. Adolescent report of greater importance placed on thinness was associated with parent report of lower self-esteem. Conclusions Findings indicate that several parent and family variables are associated with weight control behaviors, episodes of overeating, and body satisfaction and importance of thinness among overweight adolescents. Parent weight control behaviors and adolescent cognitions about body image may be important variables to target within intervention research and treatment programs for overweight youth. PMID:20708565

  12. Green Space and Child Weight Status: Does Outcome Measurement Matter? Evidence from an Australian Longitudinal Study

    Directory of Open Access Journals (Sweden)

    Taren Sanders

    2015-01-01

    Full Text Available Objective. To examine whether neighbourhood green space is beneficially associated with (i waist circumference (WC and (ii waist-to-height ratio (WtHR across childhood. Methods. Gender-stratified multilevel linear regressions were used to examine associations between green space and objective measures of weight status in the Longitudinal Study of Australian Children, a nationally representative source of data on 4,423 children aged 6 y to 13 y. WC and WtHR were measured objectively. Percentage green space within the local area of residence was calculated. Effect modification by age was explored, adjusting for socioeconomic confounding. Results. Compared to peers with 0–5% green space locally, boys and girls with >40% green space tended to have lower WC (βboys  −1.15, 95% CI −2.44, 0.14; βgirls  −0.21, 95% CI −1.47, 1.05 and WtHR (βboys  −0.82, 95% CI −1.65, 0.01; βgirls  −0.32, 95% CI −1.13, 0.49. Associations among boys were contingent upon age (p  valuesage∗green  space40% green space at 73.85 cm and 45.75% compared to those with 0–5% green space at 75.18 cm and 46.62%, respectively. Conclusions. Greener neighbourhoods appear beneficial to alternative child weight status measures, particularly among boys.

  13. Key Factors Affecting the Price of Airbnb Listings: A Geographically Weighted Approach

    Directory of Open Access Journals (Sweden)

    Zhihua Zhang

    2017-09-01

    Full Text Available Airbnb has been increasingly gaining popularity since 2008 due to its low prices and direct interactions with the local community. This paper employed a general linear model (GLM and a geographically weighted regression (GWR model to identify the key factors affecting Airbnb listing prices using data sets of 794 samples of Airbnb listings of business units in Metro Nashville, Tennessee. The results showed that the GWR model performs better than the GLM in terms of accuracy and affected variable selections. Statistically significant differences varied across regions in Metro Nashville. The coefficients illustrate a decreasing trend while there is an increase in the distance from the listed units to the convention center, which indicates that Airbnb listing prices are more sensitive to the distance from the convention center in the central area than in other areas. These findings can also provide implications for stakeholders such as Airbnb hosts to gain a better understanding of the market situation and formulate a suitable pricing strategy.

  14. Self-perception of weight status and its association with weight-related knowledge, attitudes, and behaviors among Chinese children in Guangzhou.

    Science.gov (United States)

    Cai, Li; Zhang, Ting; Ma, Jun; Ma, Lu; Jing, Jin; Chen, Yajun

    2017-07-01

    How weight perception influences weight-related knowledge, attitudes, and behaviors in Chinese children is unknown. We investigated self-perception of body weight and its correlates, and analyzed the relationship between weight perception and weight-related knowledge, attitudes, and behaviors in children in Guangzhou, China. We assessed self-reported weight perception, weight-related knowledge, attitudes, and behaviors in 3752 children aged 7-12 years. Underweight or overweight was defined using the Chinese criteria based on body mass index (BMI). Binary logistic regression analyses were performed to assess correlates of weight underestimation. In total, 27.3% of children underestimated and 6.7% overestimated their weight status. Weight underestimation was common among normal-weight (34.1%) and overweight children (25.3%). Older age, female sex, and child BMI z-score were negatively associated with normal-weight children's underestimation, whereas older age, paternal obesity, maternal obesity, and child BMI z-score were negatively associated with overweight children's underestimation. Correct answers on weight-related knowledge questions ranged from 81.5% to 98.6% and did not differ by weight perception within BMI categories. Although negative perceivers (i.e., those who perceived themselves as underweight or overweight) had a higher intention to change weight, they behaved more unhealthily on fruit intake, breakfast, screen time, and daily moderate-to-vigorous physical activities time than counterparts. Weight underestimation was prevalent in normal-weight and overweight children in Guangzhou. Negative perceivers had stronger willingness to change weight but tended to behave more unhealthily on certain behaviors than positive perceivers. Childhood obesity interventions should incorporate health education and practical support to promote healthy eating and physical activity. Copyright © 2017 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

  15. Disordered Weight Management Behaviors, Nonprescription Steroid Use, and Weight Perception in Transgender Youth.

    Science.gov (United States)

    Guss, Carly E; Williams, David N; Reisner, Sari L; Austin, S Bryn; Katz-Wise, Sabra L

    2017-01-01

    Disordered weight management behaviors are prevalent among youth; recent case reports suggested that these behaviors might also be common in transgender youth. We studied associations of gender identity with disordered weight management behaviors, nonprescription steroid use, and weight perception among transgender and cisgender (nontransgender) high-school students in Massachusetts. Data were analyzed from the 2013 Massachusetts Youth Health Survey, an anonymous survey in a random sample of Massachusetts public high schools. Respondents were divided into three groups: transgender (n = 67), cisgender male (n = 1,117), and cisgender female (n = 1,289). Fisher's exact tests and multivariable logistic regression models were used to examine unhealthy weight management behaviors in the past 30 days: fasting >24 hours, vomiting, diet pill use, and laxative use; nonprescription steroid use; and self-perceived weight status. Analyses controlled for age, race/ethnicity, and body mass index. Compared with cisgender males, transgender adolescents had higher odds of fasting >24 hours (adjusted odds ratio [AOR] = 2.9, confidence interval [CI] = 1.1-7.8), using diet pills (AOR = 8.9, 95% CI = 2.3-35.2) and taking laxatives (AOR = 7.2, 95% CI = 1.4-38.4). Transgender youth had higher odds of lifetime use of steroids without a prescription than male cisgender respondents (AOR = 26.6, 95% CI = 3.5-200.1). Compared with cisgender females, transgender respondents had higher odds of perceiving themselves as healthy weight/underweight when they were overweight/obese (AOR = 2.4, 95% CI = 1.5-4.1). Transgender youth disproportionately self-reported unsafe weight management behaviors and nonprescription steroid use compared with cisgender youth. Clinicians should be aware of this increased risk among transgender youth. Research is needed to further understand these disparities and to inform future interventions. Copyright © 2016 Society for Adolescent Health and

  16. Biochemical, Anthropometric and Lifestyle Factors Related with Weight Maintenance after Weight Loss Secondary to a Hypocaloric Mediterranean Diet.

    Science.gov (United States)

    de Luis, Daniel Antonio; Izaola, Olatz; Primo, David; Ovalle, Hilda F; Lopez, Juan Jose; Gomez, Emilia; Ortola, Ana; Aller, Rocio

    2017-01-01

    The aim of our study was to evaluate the influence of lifestyle factors and molecular biomarkers on the maintenance of the weight lost after a hypocaloric Mediterranean diet. After 3 months on a diet, patients (n = 335) remained with no controlled diet during 3 years and they were revaluated. Using linear regression, in the group of responders, we detected that a positive weight loss at 3 months, serum levels of leptin at 3 months, and each 30 min per week of physical activity were associated with weight loss maintenance. In the model with reduced weight (RW) as dependent variable, a positive weight loss at 3 months was associated with 2.4% RW (95% CI 1.31-8.11; p = 0.015), each unit of serum leptin levels at 3 months with -0.44% RW (95% CI -0.59 to -0.020; p = 0.007), each basal unit homeostasis model assessment for insulin resistance (HOMA-IR) level with -2.32% (95% CI -13.01 to -0.17; p = 0.040), and each 30 min per week of physical activity with 1.58% RW (95% CI 1.08-2.94; p = 0.020). Obese subjects who are on maintenance weight loss after a dietary intervention appear to have a better initial response during the 3 months intervention, more physical activity at 3 years, and lower basal HOMA-IR and leptin after weight loss than those who regain weight. © 2017 S. Karger AG, Basel.

  17. Regression analysis by example

    CERN Document Server

    Chatterjee, Samprit

    2012-01-01

    Praise for the Fourth Edition: ""This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable."" -Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded

  18. Exploring spatial patterns in the associations between local AIDS incidence and socioeconomic and demographic variables in the state of Rio de Janeiro, Brazil.

    Science.gov (United States)

    Alves, André T J; Nobre, Flavio F; Waller, Lance A

    2016-05-01

    Access to antiretroviral therapy (ART), universally provided in Brazil since 1996, resulted in a reduction in overall morbidity and mortality due to AIDS or AIDS-related complications, but in some municipalities of Rio de Janeiro, AIDS incidence remains high. Public health surveillance remains an invaluable tool for understanding current AIDS epidemiologic patterns and local socioeconomic and demographic factors associated with increased incidence. Geographically Weighted Poisson Regression (GWPR) explores spatial varying impacts of these factors across the study area focusing attention on local variations in ecological associations. The set of sociodemographic variables under consideration revealed significant associations with local AIDS incidence and these associations varied geographically across the study area. We find the effects of predictors on AIDS incidence are not constant across the state, contrary to assumptions in the global models. We observe and quantify different local factors driving AIDS incidence in different parts of the state. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  19. Applied logistic regression

    CERN Document Server

    Hosmer, David W; Sturdivant, Rodney X

    2013-01-01

     A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-

  20. Genetic analyses of partial egg production in Japanese quail using multi-trait random regression models.

    Science.gov (United States)

    Karami, K; Zerehdaran, S; Barzanooni, B; Lotfi, E

    2017-12-01

    1. The aim of the present study was to estimate genetic parameters for average egg weight (EW) and egg number (EN) at different ages in Japanese quail using multi-trait random regression (MTRR) models. 2. A total of 8534 records from 900 quail, hatched between 2014 and 2015, were used in the study. Average weekly egg weights and egg numbers were measured from second until sixth week of egg production. 3. Nine random regression models were compared to identify the best order of the Legendre polynomials (LP). The most optimal model was identified by the Bayesian Information Criterion. A model with second order of LP for fixed effects, second order of LP for additive genetic effects and third order of LP for permanent environmental effects (MTRR23) was found to be the best. 4. According to the MTRR23 model, direct heritability for EW increased from 0.26 in the second week to 0.53 in the sixth week of egg production, whereas the ratio of permanent environment to phenotypic variance decreased from 0.48 to 0.1. Direct heritability for EN was low, whereas the ratio of permanent environment to phenotypic variance decreased from 0.57 to 0.15 during the production period. 5. For each trait, estimated genetic correlations among weeks of egg production were high (from 0.85 to 0.98). Genetic correlations between EW and EN were low and negative for the first two weeks, but they were low and positive for the rest of the egg production period. 6. In conclusion, random regression models can be used effectively for analysing egg production traits in Japanese quail. Response to selection for increased egg weight would be higher at older ages because of its higher heritability and such a breeding program would have no negative genetic impact on egg production.

  1. Normalization Ridge Regression in Practice I: Comparisons Between Ordinary Least Squares, Ridge Regression and Normalization Ridge Regression.

    Science.gov (United States)

    Bulcock, J. W.

    The problem of model estimation when the data are collinear was examined. Though the ridge regression (RR) outperforms ordinary least squares (OLS) regression in the presence of acute multicollinearity, it is not a problem free technique for reducing the variance of the estimates. It is a stochastic procedure when it should be nonstochastic and it…

  2. Weight-Control Methods, 3-Year Weight Change, and Eating Behaviors: A Prospective Nationwide Study of Middle-Aged New Zealand Women.

    Science.gov (United States)

    Leong, Sook Ling; Gray, Andrew; Haszard, Jillian; Horwath, Caroline

    2016-08-01

    The effectiveness of women's weight-control methods and the influences of dieting on eating behaviors remain unclear. Our aim was to determine the association of various weight-control methods at baseline with weight change to 3 years, and examine the association between baseline weight-control status (trying to lose weight, trying to prevent weight gain or no weight-control attempts) and changes in intuitive eating and binge eating at 3 years. A nationally representative sample of 1,601 New Zealand women (40 to 50 years) was recruited and completed a self-administered questionnaire at baseline regarding use of variety of weight-control methods. Information on demographic characteristics, weight, height, food habits, binge eating, and intuitive eating were collected at baseline and 3 years. Linear and logistic regression models examined associations between both weight status and weight-control methods at baseline and weight change to 3 years; and baseline weight-control status and change in intuitive eating from baseline to 3 years and binge eating at 3 years. χ(2) tests were used to cross-sectionally compare food habits across the weight status categories at both baseline and 3 years. Trying to lose weight and the use of weight-control methods at baseline were not associated with change in body weight to 3 years. There were a few differences in the frequency of consumption of high-energy-density foods between those trying to lose or maintain weight and those not attempting weight control. Trying to lose weight at baseline was associated with a 2.0-unit (95% CI 0.7 to 3.4, P=0.003) reduction in intuitive eating scores by 3 years (potential range=21 to 105), and 224% (odds ratio=3.24; 95% CI 1.69 to 6.20; Pfoods. Dieting may reduce women's ability to recognize hunger and satiety cues and place women at increased risk of binge eating. Copyright © 2016 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

  3. Vector regression introduced

    Directory of Open Access Journals (Sweden)

    Mok Tik

    2014-06-01

    Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.

  4. Urinary incontinence and weight changes during pregnancy and post partum: a pending challenge.

    Science.gov (United States)

    Ruiz de Viñaspre Hernández, Regina; Rubio Aranda, Encarnación; Tomás Aznar, Concepción

    2013-12-01

    to analyse the association between urinary incontinence and maternal weight, and its variations in pregnancy and post partum. observational study of a cohort of women from the start of pregnancy until six months post partum. Hospital San Pedro in La Rioja, Spain. 402 pregnant women without urinary incontinence at the start of pregnancy. the dependent variable was urinary incontinence, assessed using the Urogenital Distress Inventory-Short Form questionnaire. The main independent variables were body mass index (BMI) at the first antenatal visit and six months post partum, weight gain during pregnancy, postpartum weight loss, and weight retained from the start of pregnancy to six months post partum. The association between urinary incontinence and the main independent variables was measured using Student's t-test. Three simple logistic regression models were used to assess the strength of this association, one for each of the independent variables that showed a significant association with urinary incontinence (p<0.05), and three multiple regression models that included the possible variable effect modifiers were also used. At the start of pregnancy, 20.1% of the women were overweight and 8.7% were obese. Six months post partum, 30.3% of the women were overweight and 11.4% were obese. The mean (±standard deviation) retained weight was 2 (±3.1) kg. Postpartum urinary incontinence was associated with BMI at six months post partum, postpartum weight loss and retained weight at six months post partum (p<0.05). The association of urinary incontinence with these variables was significant, and remained stable in both simple and multiple regression analyses with BMI at six months post partum [odds ratio (OR) 1.09 versus 1.08], weight loss from delivery to six months post partum (OR 0.88 versus 0.88), and retained weight from the beginning of pregnancy until six months post partum (OR 1.23 versus 1.19). high BMI and weight retention at six months post partum increase the

  5. Fetal growth in relation to gestational weight gain in women with Type 2 diabetes

    DEFF Research Database (Denmark)

    Parellada, C B; Asbjörnsdóttir, Björg; Ringholm, Lene

    2014-01-01

    AIMS: To evaluate fetal growth in relation to gestational weight gain in women with Type 2 diabetes. METHODS: A retrospective cohort study of 142 consecutive pregnancies in 28 women of normal weight, 39 overweight women and 75 obese women with Type 2 diabetes (pre-pregnancy BMI .../week, respectively. In multiple linear regression analysis, gestational weight gain was associated with a higher infant birth weight z-score independent of pre-pregnancy BMI, smoking, HbA1c and insulin dose at last visit, ethnicity and parity [β=0.1 (95% CI 0.06-0.14), P

  6. Neighborhood racial composition and poverty in association with pre-pregnancy weight and gestational weight gain.

    Science.gov (United States)

    Mendez, Dara D; Thorpe, Roland J; Amutah, Ndidi; Davis, Esa M; Walker, Renee E; Chapple-McGruder, Theresa; Bodnar, Lisa

    2016-12-01

    Studies of neighborhood racial composition or neighborhood poverty in association with pregnancy-related weight are limited. Prior studies of neighborhood racial density and poverty has been in association with adverse birth outcomes and suggest that neighborhoods with high rates of poverty and racial composition of black residents are typically segregated and systematically isolated from opportunities and resources. These neighborhood factors may help explain the racial disparities in pre-pregnancy weight and inadequate weight gain. This study examined whether neighborhood racial composition and neighborhood poverty was associated with weight before pregnancy and weight gain during pregnancy and if this association differed by race. We used vital birth records of singleton births of 73,061 non-Hispanic black and white women in Allegheny County, PA (2003-2010). Maternal race and ethnicity, pre-pregnancy body-mass-index (BMI), gestational weight gain and other individual-level characteristics were derived from vital birth record data, and measures of neighborhood racial composition (percentage of black residents in the neighborhood) and poverty (percentage of households in the neighborhood below the federal poverty) were derived using US Census data. Multilevel log binomial regression models were performed to estimate neighborhood racial composition and poverty in association with pre-pregnancy weight (i.e., overweight/obese) and gestational weight gain (i.e., inadequate and excessive). Black women as compared to white women were more likely to be overweight/obese before pregnancy and to have inadequate gestational weight gain (53.6% vs. 38.8%; 22.5% vs. 14.75 respectively). Black women living in predominately black neighborhoods were slightly more likely to be obese prior to pregnancy compared to black women living in predominately white neighborhoods (PR 1.10; 95% CI: 1.03, 1.16). Black and white women living in high poverty areas compared with women living in

  7. Applied linear regression

    CERN Document Server

    Weisberg, Sanford

    2013-01-01

    Praise for the Third Edition ""...this is an excellent book which could easily be used as a course text...""-International Statistical Institute The Fourth Edition of Applied Linear Regression provides a thorough update of the basic theory and methodology of linear regression modeling. Demonstrating the practical applications of linear regression analysis techniques, the Fourth Edition uses interesting, real-world exercises and examples. Stressing central concepts such as model building, understanding parameters, assessing fit and reliability, and drawing conclusions, the new edition illus

  8. Cord Blood Metabolome Is Highly Associated with Birth Weight, but Less Predictive for Later Weight Development

    Directory of Open Access Journals (Sweden)

    Christian Hellmuth

    2017-04-01

    Full Text Available Background/Aims: Fetal metabolism may be changed by the exposure to maternal factors, and the route to obesity may already set in utero. Cord blood metabolites might predict growth patterns and later obesity. We aimed to characterize associations of cord blood with birth weight, postnatal weight gain, and BMI in adolescence. Methods: Over 700 cord blood samples were collected from infants participating in the German birth cohort study LISAplus. Glycerophospholipid fatty acids (GPL-FA, polar lipids, non-esterified fatty acids (NEFA, and amino acids were analyzed with a targeted, liquid chromatography-tandem mass spectrometry based metabolomics platform. Cord blood metabolites were related to growth factors by linear regression models adjusted for confounding variables. Results: Cord blood metabolites were highly associated with birth weight. Lysophosphatidylcholines C16:1, C18:1, C20:3, C18:2, C20:4, C14:0, C16:0, C18:3, GPL-FA C20:3n-9, and GPL-FA C22:5n-6 were positively related to birth weight, while higher cord blood concentrations of NEFA C22:6, NEFA C20:5, GPL-FA C18:3n-3, and PCe C38:0 were associated with lower birth weight. Postnatal weight gain and BMI z-scores in adolescents were not significantly associated with cord blood metabolites after adjustment for multiple testing. Conclusion: Potential long-term programming effects of the intrauterine environment and metabolism on later health cannot be predicted with profiling of the cord blood metabolome.

  9. A comparative study on generating simulated Landsat NDVI images using data fusion and regression method-the case of the Korean Peninsula.

    Science.gov (United States)

    Lee, Mi Hee; Lee, Soo Bong; Eo, Yang Dam; Kim, Sun Woong; Woo, Jung-Hun; Han, Soo Hee

    2017-07-01

    Landsat optical images have enough spatial and spectral resolution to analyze vegetation growth characteristics. But, the clouds and water vapor degrade the image quality quite often, which limits the availability of usable images for the time series vegetation vitality measurement. To overcome this shortcoming, simulated images are used as an alternative. In this study, weighted average method, spatial and temporal adaptive reflectance fusion model (STARFM) method, and multilinear regression analysis method have been tested to produce simulated Landsat normalized difference vegetation index (NDVI) images of the Korean Peninsula. The test results showed that the weighted average method produced the images most similar to the actual images, provided that the images were available within 1 month before and after the target date. The STARFM method gives good results when the input image date is close to the target date. Careful regional and seasonal consideration is required in selecting input images. During summer season, due to clouds, it is very difficult to get the images close enough to the target date. Multilinear regression analysis gives meaningful results even when the input image date is not so close to the target date. Average R 2 values for weighted average method, STARFM, and multilinear regression analysis were 0.741, 0.70, and 0.61, respectively.

  10. The Impact of Parents’ Categorization of Their Own Weight and Their Child’s Weight on Healthy Lifestyle Promoting Beliefs and Practices

    Directory of Open Access Journals (Sweden)

    Allison C. Sylvetsky-Meni

    2015-01-01

    Full Text Available Objective. To evaluate parents’ beliefs and practices related to childhood obesity and determine if these are influenced by parent’s perception of their own weight or their child’s weight. Methods. Parents of obese (n=689 or normal weight (n=1122 children 4–15 years in Georgia, USA, were randomly selected to complete a telephone survey. Frequency of child obesity-related perceptions, beliefs, and practices were assessed, stratified by parent-perceived self-weight and child weight status, and compared using Chi-squared tests and multivariate logistic regression. Results. Most parents, regardless of perceived child weight, agreed that child overweight/obesity can cause serious illness (95% but only one-half believed it was a problem in Georgia. Many (42.4% failed to recognize obesity in their own children. More parents who perceived their child as overweight versus normal weight reported concern about their child’s diet and activity and indicated readiness for lifestyle change. Parents’ perception of their own weight had little additional impact. Conclusions. While awareness of child overweight as a modifiable health risk is high, many parents fail to recognize it in their own families and communities, reducing the likelihood of positive lifestyle change. Additional efforts to help parents understand their role in facilitating behavior change and to assist them in identifying at-risk children are required.

  11. The CHOP postnatal weight gain, birth weight, and gestational age retinopathy of prematurity risk model.

    Science.gov (United States)

    Binenbaum, Gil; Ying, Gui-Shuang; Quinn, Graham E; Huang, Jiayan; Dreiseitl, Stephan; Antigua, Jules; Foroughi, Negar; Abbasi, Soraya

    2012-12-01

    To develop a birth weight (BW), gestational age (GA), and postnatal-weight gain retinopathy of prematurity (ROP) prediction model in a cohort of infants meeting current screening guidelines. Multivariate logistic regression was applied retrospectively to data from infants born with BW less than 1501 g or GA of 30 weeks or less at a single Philadelphia hospital between January 1, 2004, and December 31, 2009. In the model, BW, GA, and daily weight gain rate were used repeatedly each week to predict risk of Early Treatment of Retinopathy of Prematurity type 1 or 2 ROP. If risk was above a cut-point level, examinations would be indicated. Of 524 infants, 20 (4%) had type 1 ROP and received laser treatment; 28 (5%) had type 2 ROP. The model (Children's Hospital of Philadelphia [CHOP]) accurately predicted all infants with type 1 ROP; missed 1 infant with type 2 ROP, who did not require laser treatment; and would have reduced the number of infants requiring examinations by 49%. Raising the cut point to miss one type 1 ROP case would have reduced the need for examinations by 79%. Using daily weight measurements to calculate weight gain rate resulted in slightly higher examination reduction than weekly measurements. The BW-GA-weight gain CHOP ROP model demonstrated accurate ROP risk assessment and a large reduction in the number of ROP examinations compared with current screening guidelines. As a simple logistic equation, it can be calculated by hand or represented as a nomogram for easy clinical use. However, larger studies are needed to achieve a highly precise estimate of sensitivity prior to clinical application.

  12. Effect of prenatal exposure to kitchen fuel on birth weight

    Directory of Open Access Journals (Sweden)

    Yugantara Ramesh Kadam

    2013-01-01

    Full Text Available Background: Maternal exposure to kitchen fuel smoke may lead to impaired fetal growth. Objective: To study the effect of exposure to various kitchen fuels on birth weight. Methodology : Study type: Retrospective analytical. Study setting: Hospital based. Study Subjects: Mothers and their newborns. Inclusion Criteria: Mothers registered in first trimester with minimum 3 visits, non-anemic, full-term, and singleton delivery. Exclusion Criteria: History of Pregnancy Induced Hypertension (PIH, Diabetes Mellitus (DM, tobacco chewers or mishri users. Sample size: 328 mothers and their new-borne. Study period: Six months. Study tools: Chi-square, Z-test, ANOVA, and binary logistic regression. Results: Effect of confounders on birth weight was tested and found to be non-significant. Mean ± SD of birth weight was 2.669 ± 0.442 in Liquid Petroleium Gas (LPG users (n = 178, 2.465 ± 0.465 in wood users (n = 94, 2.557 ± 0.603 in LPG + wood users (n = 27 and 2.617 ± 0.470 in kerosene users (n = 29. Infants born to wood users had lowest birth weight and averagely 204 g lighter than LPG users (F = 4.056, P < 0.01. Percentage of newborns with low birth weight (LBW in wood users was 44.68% which was significantly higher than in LPG users (24.16%, LPG + wood users (40.74% and in kerosene users (34.48% (Chi-square = 12.926, P < 0.01. As duration of exposure to wood fuel increases there is significant decline in birth weight (F = 3.825, P < 0.05. By using logistic regression type of fuel is only best predictor. Conclusion: Cooking with wood fuel is a significant risk-factor for LBW, which is modifiable.

  13. Expert Coaching in Weight Loss: Retrospective Analysis

    Science.gov (United States)

    Kushner, Robert F; Hill, James O; Lindquist, Richard; Brunning, Scott; Margulies, Amy

    2018-01-01

    Background Providing coaches as part of a weight management program is a common practice to increase participant engagement and weight loss success. Understanding coach and participant interactions and how these interactions impact weight loss success needs to be further explored for coaching best practices. Objective The purpose of this study was to analyze the coach and participant interaction in a 6-month weight loss intervention administered by Retrofit, a personalized weight management and Web-based disease prevention solution. The study specifically examined the association between different methods of coach-participant interaction and weight loss and tried to understand the level of coaching impact on weight loss outcome. Methods A retrospective analysis was performed using 1432 participants enrolled from 2011 to 2016 in the Retrofit weight loss program. Participants were males and females aged 18 years or older with a baseline body mass index of ≥25 kg/m², who also provided at least one weight measurement beyond baseline. First, a detailed analysis of different coach-participant interaction was performed using both intent-to-treat and completer populations. Next, a multiple regression analysis was performed using all measures associated with coach-participant interactions involving expert coaching sessions, live weekly expert-led Web-based classes, and electronic messaging and feedback. Finally, 3 significant predictors (Pcoaching session attendance (Pcoaching sessions, attending 60% of live weekly Web-based classes, and receiving a minimum of 1 food log feedback day per week were associated with clinically significant weight loss. Conclusions Participant’s one-on-one expert coaching session attendance, live weekly expert-led interactive Web-based class attendance, and the number of food log feedback days per week from expert coach were significant predictors of weight loss in a 6-month intervention. PMID:29535082

  14. An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

    Directory of Open Access Journals (Sweden)

    J. Chardon

    2018-01-01

    Full Text Available Statistical downscaling models (SDMs are often used to produce local weather scenarios from large-scale atmospheric information. SDMs include transfer functions which are based on a statistical link identified from observations between local weather and a set of large-scale predictors. As physical processes driving surface weather vary in time, the most relevant predictors and the regression link are likely to vary in time too. This is well known for precipitation for instance and the link is thus often estimated after some seasonal stratification of the data. In this study, we present a two-stage analog/regression model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are identified from fields of geopotential heights at 1000 and 500 hPa. For the regression stage, two generalized linear models are further used to model the probability of precipitation occurrence and the distribution of non-zero precipitation amounts, respectively. The two-stage model is evaluated for the probabilistic prediction of small-scale precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and amount. As the analog days vary from one prediction day to another, the atmospheric predictors selected in the regression stage and the value of the corresponding regression coefficients can vary from one prediction day to another. The model allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations.

  15. Cuisine Preference of Local Tourists in San Juan, Batangas, Philippines

    Directory of Open Access Journals (Sweden)

    RYENE SELLINE B. KALALO

    2014-08-01

    Full Text Available This study aimed to determine the cuisine preference of the local tourist in San Juan, Batangas. More specifically, it aimed to describe the demographic profile of local tourist; to identify the preferred cuisine by different restaurants; to determine the significant difference when group according to demographic profile; and to determine the cuisine preference of local tourists in San Juan, Batangas. The research design used the descriptive method because it is the most appropriate method. It was found that the over-all assessment was frequent. Hamburger received the highest weighted mean followed by Sandwiches interpreted as frequent. Doughnut and Roasted Turkey got the lowest. Chinese Cuisine is frequently served. Lumpiang Shanghai has the highest weighted mean that is frequently offered and Siomai being the second highest. Siopao and Dumpling got the lowest weighted mean that makes it sometimes offered in every restaurant. Japanese cuisine has an over-all assessment of frequent. Tempura has the highest weighted mean followed by Teriyaki. Ramen has the second to the lowest weighted mean and Tonkatsu got the lowest. French Cuisine has a composite mean with an over-all assessment of sometimes. Mediterranean salad has the highest weighted followed by French Macaroons. Lamb and Ratatouille has the lowest weighted mean

  16. Incremental value of diffusion weighted and dynamic contrast enhanced MRI in the detection of locally recurrent prostate cancer after radiation treatment: preliminary results

    International Nuclear Information System (INIS)

    Akin, Oguz; Vargas, Hebert Alberto; Hricak, Hedvig; Gultekin, David H.; Zheng, Junting; Moskowitz, Chaya; Pei, Xin; Sperling, Dahlia; Zelefsky, Michael J.; Schwartz, Lawrence H.

    2011-01-01

    To assess the incremental value of diffusion-weighted (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI) to T2-weighted MRI (T2WI) in detecting locally recurrent prostate cancer after radiotherapy. Twenty-four patients (median age, 70 years) with a history of radiotherapy-treated prostate cancer underwent multi-parametric MRI (MP-MRI) and transrectal prostate biopsy. Two readers independently scored the likelihood of cancer on a 1-5 scale, using T2WI alone and then adding DW-MRI and DCE-MRI. Areas under receiver operating characteristic curves (AUCs) were estimated at the patient and prostate-side levels. The apparent diffusion coefficient (ADC) from DW-MRI and the K trans , k ep , v e , AUGC90 and AUGC180 from DCE-MRI were recorded. Biopsy was positive in 16/24 (67%) and negative in 8/24 (33%) patients. AUCs for readers 1 and 2 increased from 0.64 and 0.53 to 0.95 and 0.86 with MP-MRI, at the patient level, and from 0.73 and 0.66 to 0.90 and 0.79 with MP-MRI, at the prostate-side level (p values -3 mm 2 /s)], median K trans [1.07 vs. 0.34 (1/min)], and k ep [2.06 vs 1.0 (1/min)] (p values < 0.05). MP-MRI was significantly more accurate than T2WI alone in detecting locally recurrent prostate cancer after radiotherapy. (orig.)

  17. Understanding poisson regression.

    Science.gov (United States)

    Hayat, Matthew J; Higgins, Melinda

    2014-04-01

    Nurse investigators often collect study data in the form of counts. Traditional methods of data analysis have historically approached analysis of count data either as if the count data were continuous and normally distributed or with dichotomization of the counts into the categories of occurred or did not occur. These outdated methods for analyzing count data have been replaced with more appropriate statistical methods that make use of the Poisson probability distribution, which is useful for analyzing count data. The purpose of this article is to provide an overview of the Poisson distribution and its use in Poisson regression. Assumption violations for the standard Poisson regression model are addressed with alternative approaches, including addition of an overdispersion parameter or negative binomial regression. An illustrative example is presented with an application from the ENSPIRE study, and regression modeling of comorbidity data is included for illustrative purposes. Copyright 2014, SLACK Incorporated.

  18. Endogenous Generalized Weights under DEA Control

    DEFF Research Database (Denmark)

    Agrell, Per J.; Bogetoft, Peter

    Non-parametric efficiency analysis, such as Data Envelopment Analysis (DEA) relies so far on endogenous local or exogenous general weights, based on revealed preferences or market prices. However, as DEA is gaining popularity in regulation and normative budgeting, the strategic interest...... of the evaluated industry calls for attention. We offer endogenous general prices based on a reformulation of DEA where the units collectively propose the set of weights that maximize their efficiency. Thus, the sector-wide efficiency is then a result of compromising the scores of more specialized smaller units...

  19. Alternative Methods of Regression

    CERN Document Server

    Birkes, David

    2011-01-01

    Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data s

  20. Wind Tunnel Strain-Gage Balance Calibration Data Analysis Using a Weighted Least Squares Approach

    Science.gov (United States)

    Ulbrich, N.; Volden, T.

    2017-01-01

    A new approach is presented that uses a weighted least squares fit to analyze wind tunnel strain-gage balance calibration data. The weighted least squares fit is specifically designed to increase the influence of single-component loadings during the regression analysis. The weighted least squares fit also reduces the impact of calibration load schedule asymmetries on the predicted primary sensitivities of the balance gages. A weighting factor between zero and one is assigned to each calibration data point that depends on a simple count of its intentionally loaded load components or gages. The greater the number of a data point's intentionally loaded load components or gages is, the smaller its weighting factor becomes. The proposed approach is applicable to both the Iterative and Non-Iterative Methods that are used for the analysis of strain-gage balance calibration data in the aerospace testing community. The Iterative Method uses a reasonable estimate of the tare corrected load set as input for the determination of the weighting factors. The Non-Iterative Method, on the other hand, uses gage output differences relative to the natural zeros as input for the determination of the weighting factors. Machine calibration data of a six-component force balance is used to illustrate benefits of the proposed weighted least squares fit. In addition, a detailed derivation of the PRESS residuals associated with a weighted least squares fit is given in the appendices of the paper as this information could not be found in the literature. These PRESS residuals may be needed to evaluate the predictive capabilities of the final regression models that result from a weighted least squares fit of the balance calibration data.

  1. Elevated plasma urokinase receptor predicts low birth weight in maternal malaria

    DEFF Research Database (Denmark)

    Ostrowski, S R; Shulman, C E; Peshu, N

    2007-01-01

    -suPAR and gestational age were the only independent predictors of birth weight in multivariate linear regression adjusted for maternal-suPAR, HIV-1 infection, age, BMI, haemoglobin, peripheral parasitaemia, parity and gestational age; 1 ng/mL higher maternal-suPAR predicted -56 g (95% CI -100 to -12, P = 0.016) reduced...... birth weight. Cord-suPAR could not predict birth weight after adjusting for gestational age. Future studies are warranted to investigate whether the maternal suPAR level is increased earlier in pregnancy in women with active placental malaria infection and whether early maternal suPAR measurements can...... predict birth weight. If so, measurements of maternal suPAR early in pregnancy might then potentially identify women with increased needs for antenatal care and intervention....

  2. Genetic Parameters for Body condition score, Body weigth, Milk yield and Fertility estimated using random regression models

    NARCIS (Netherlands)

    Berry, D.P.; Buckley, F.; Dillon, P.; Evans, R.D.; Rath, M.; Veerkamp, R.F.

    2003-01-01

    Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields

  3. TU-AB-BRA-03: Atlas-Based Algorithms with Local Registration-Goodness Weighting for MRI-Driven Electron Density Mapping

    International Nuclear Information System (INIS)

    Farjam, R; Tyagi, N; Veeraraghavan, H; Apte, A; Zakian, K; Deasy, J; Hunt, M

    2016-01-01

    Purpose: To develop image-analysis algorithms to synthesize CT with accurate electron densities for MR-only radiotherapy of head & neck (H&N) and pelvis anatomies. Methods: CT and 3T-MRI (Philips, mDixon sequence) scans were randomly selected from a pool of H&N (n=11) and pelvis (n=12) anatomies to form an atlas. All MRIs were pre-processed to eliminate scanner and patient-induced intensity inhomogeneities and standardize their intensity histograms. CT and MRI for each patient were then co-registered to construct CT-MRI atlases. For more accurate CT-MR fusion, bone intensities in CT were suppressed to improve the similarity between CT and MRI. For a new patient, all CT-MRI atlases are deformed onto the new patients’ MRI initially. A newly-developed generalized registration error (GRE) metric was then calculated as a measure of local registration accuracy. The synthetic CT value at each point is a 1/GRE-weighted average of CTs from all CT-MR atlases. For evaluation, the mean absolute error (MAE) between the original and synthetic CT (generated in a leave-one-out scheme) was computed. The planning dose from the original and synthetic CT was also compared. Results: For H&N patients, MAE was 67±9, 114±22, and 116±9 HU over the entire-CT, air and bone regions, respectively. For pelvis anatomy, MAE was 47±5 and 146±14 for the entire and bone regions. In comparison with MIRADA medical, an FDA-approved registration tool, we found that our proposed registration strategy reduces MAE by ∼30% and ∼50% over the entire and bone regions, respectively. GRE-weighted strategy further lowers MAE by ∼15% to ∼40%. Our primary dose calculation also showed highly consistent results between the original and synthetic CT. Conclusion: We’ve developed a novel image-analysis technique to synthesize CT for H&N and pelvis anatomies. Our proposed image fusion strategy and GRE metric help generate more accurate synthetic CT using locally more similar atlases (Support: Philips

  4. TU-AB-BRA-03: Atlas-Based Algorithms with Local Registration-Goodness Weighting for MRI-Driven Electron Density Mapping

    Energy Technology Data Exchange (ETDEWEB)

    Farjam, R; Tyagi, N [Memorial Sloan-Kettering Cancer Center, New York, NY (United States); Veeraraghavan, H; Apte, A; Zakian, K; Deasy, J [Memorial Sloan Kettering Cancer Center, New York, NY (United States); Hunt, M [Mem Sloan-Kettering Cancer Center, New York, NY (United States)

    2016-06-15

    Purpose: To develop image-analysis algorithms to synthesize CT with accurate electron densities for MR-only radiotherapy of head & neck (H&N) and pelvis anatomies. Methods: CT and 3T-MRI (Philips, mDixon sequence) scans were randomly selected from a pool of H&N (n=11) and pelvis (n=12) anatomies to form an atlas. All MRIs were pre-processed to eliminate scanner and patient-induced intensity inhomogeneities and standardize their intensity histograms. CT and MRI for each patient were then co-registered to construct CT-MRI atlases. For more accurate CT-MR fusion, bone intensities in CT were suppressed to improve the similarity between CT and MRI. For a new patient, all CT-MRI atlases are deformed onto the new patients’ MRI initially. A newly-developed generalized registration error (GRE) metric was then calculated as a measure of local registration accuracy. The synthetic CT value at each point is a 1/GRE-weighted average of CTs from all CT-MR atlases. For evaluation, the mean absolute error (MAE) between the original and synthetic CT (generated in a leave-one-out scheme) was computed. The planning dose from the original and synthetic CT was also compared. Results: For H&N patients, MAE was 67±9, 114±22, and 116±9 HU over the entire-CT, air and bone regions, respectively. For pelvis anatomy, MAE was 47±5 and 146±14 for the entire and bone regions. In comparison with MIRADA medical, an FDA-approved registration tool, we found that our proposed registration strategy reduces MAE by ∼30% and ∼50% over the entire and bone regions, respectively. GRE-weighted strategy further lowers MAE by ∼15% to ∼40%. Our primary dose calculation also showed highly consistent results between the original and synthetic CT. Conclusion: We’ve developed a novel image-analysis technique to synthesize CT for H&N and pelvis anatomies. Our proposed image fusion strategy and GRE metric help generate more accurate synthetic CT using locally more similar atlases (Support: Philips

  5. Higher-Than-Conventional Radiation Doses in Localized Prostate Cancer Treatment: A Meta-analysis of Randomized, Controlled Trials

    International Nuclear Information System (INIS)

    Viani, Gustavo Arruda; Stefano, Eduardo Jose; Afonso, Sergio Luis

    2009-01-01

    Purpose: To determine in a meta-analysis whether the outcomes in men with localized prostate cancer treated with high-dose radiotherapy (HDRT) are better than those in men treated with conventional-dose radiotherapy (CDRT), by quantifying the effect of the total dose of radiotherapy on biochemical control (BC). Methods and Materials: The MEDLINE, EMBASE, CANCERLIT, and Cochrane Library databases, as well as the proceedings of annual meetings, were systematically searched to identify randomized, controlled studies comparing HDRT with CDRT for localized prostate cancer. To evaluate the dose-response relationship, we conducted a meta-regression analysis of BC ratios by means of weighted linear regression. Results: Seven RCTs with a total patient population of 2812 were identified that met the study criteria. Pooled results from these RCTs showed a significant reduction in the incidence of biochemical failure in those patients with prostate cancer treated with HDRT (p 2 gastrointestinal toxicity after HDRT than after CDRT. In the subgroup analysis, patients classified as being at low (p = 0.007), intermediate (p < 0.0001), and high risk (p < 0.0001) of biochemical failure all showed a benefit from HDRT. The meta-regression analysis also detected a linear correlation between the total dose of radiotherapy and biochemical failure (BC = -67.3 + [1.8 x radiotherapy total dose in Gy]; p = 0.04). Conclusions: Our meta-analysis showed that HDRT is superior to CDRT in preventing biochemical failure in low-, intermediate-, and high-risk prostate cancer patients, suggesting that this should be offered as a treatment for all patients, regardless of their risk status.

  6. Introduction to regression graphics

    CERN Document Server

    Cook, R Dennis

    2009-01-01

    Covers the use of dynamic and interactive computer graphics in linear regression analysis, focusing on analytical graphics. Features new techniques like plot rotation. The authors have composed their own regression code, using Xlisp-Stat language called R-code, which is a nearly complete system for linear regression analysis and can be utilized as the main computer program in a linear regression course. The accompanying disks, for both Macintosh and Windows computers, contain the R-code and Xlisp-Stat. An Instructor's Manual presenting detailed solutions to all the problems in the book is ava

  7. ANYOLS, Least Square Fit by Stepwise Regression

    International Nuclear Information System (INIS)

    Atwoods, C.L.; Mathews, S.

    1986-01-01

    Description of program or function: ANYOLS is a stepwise program which fits data using ordinary or weighted least squares. Variables are selected for the model in a stepwise way based on a user- specified input criterion or a user-written subroutine. The order in which variables are entered can be influenced by user-defined forcing priorities. Instead of stepwise selection, ANYOLS can try all possible combinations of any desired subset of the variables. Automatic output for the final model in a stepwise search includes plots of the residuals, 'studentized' residuals, and leverages; if the model is not too large, the output also includes partial regression and partial leverage plots. A data set may be re-used so that several selection criteria can be tried. Flexibility is increased by allowing the substitution of user-written subroutines for several default subroutines

  8. Prognostic significance of unintentional body weight loss in colon cancer patients.

    Science.gov (United States)

    Kuo, Yi-Hung; Shi, Chung-Sheng; Huang, Cheng Yi; Huang, Yun-Ching; Chin, Chih-Chien

    2018-04-01

    The aim of the present study was to investigate whether unintentional body weight loss (BWL) provides additional clinical information in terms of tumor progression and prognosis in non-metastatic colon cancer. In the present study, a total of 2,406 consecutive colon cancer patients without metastasis were retrospectively enrolled. Unintentional BWL was defined as loss of >5% of body weight within the last 6-12 months, or defined subjectively upon fulfillment of at least two of the following: Evidence of change in clothing size and corroboration of the reported weight loss by family or friend. This category was recorded as present ('with') or absent ('without'). Logistic regression analysis was performed to determine the correlation between BWL and the tumor characteristics and post-operative outcomes of patients with colon cancer. The Cox regression model was used to determine the association of BWL with long-term survival of colon cancer patients. A significant association between BWL and tumor location [right vs. left: Odds ratio (OR)=1.62; Pcolon cancer is not just a symptom, but it is also correlated with tumor location, size and depth, and is a prognostic factor for poor outcomes including overall survival and tumor relapse.

  9. A menu-driven software package of Bayesian nonparametric (and parametric) mixed models for regression analysis and density estimation.

    Science.gov (United States)

    Karabatsos, George

    2017-02-01

    Most of applied statistics involves regression analysis of data. In practice, it is important to specify a regression model that has minimal assumptions which are not violated by data, to ensure that statistical inferences from the model are informative and not misleading. This paper presents a stand-alone and menu-driven software package, Bayesian Regression: Nonparametric and Parametric Models, constructed from MATLAB Compiler. Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear models. Each of the 78 regression models handles either a continuous, binary, or ordinal dependent variable, and can handle multi-level (grouped) data. All 83 Bayesian models can handle the analysis of weighted observations (e.g., for meta-analysis), and the analysis of left-censored, right-censored, and/or interval-censored data. Each BNP infinite-mixture model has a mixture distribution assigned one of various BNP prior distributions, including priors defined by either the Dirichlet process, Pitman-Yor process (including the normalized stable process), beta (two-parameter) process, normalized inverse-Gaussian process, geometric weights prior, dependent Dirichlet process, or the dependent infinite-probits prior. The software user can mouse-click to select a Bayesian model and perform data analysis via Markov chain Monte Carlo (MCMC) sampling. After the sampling completes, the software automatically opens text output that reports MCMC-based estimates of the model's posterior distribution and model predictive fit to the data. Additional text and/or graphical output can be generated by mouse-clicking other menu options. This includes output of MCMC convergence analyses, and estimates of the model's posterior predictive distribution, for selected

  10. Evaluation of body weight of sea cucumber Apostichopus japonicus by computer vision

    Science.gov (United States)

    Liu, Hui; Xu, Qiang; Liu, Shilin; Zhang, Libin; Yang, Hongsheng

    2015-01-01

    A postichopus japonicus (Holothuroidea, Echinodermata) is an ecological and economic species in East Asia. Conventional biometric monitoring method includes diving for samples and weighing above water, with highly variable in weight measurement due to variation in the quantity of water in the respiratory tree and intestinal content of this species. Recently, video survey method has been applied widely in biometric detection on underwater benthos. However, because of the high flexibility of A. japonicus body, video survey method of monitoring is less used in sea cucumber. In this study, we designed a model to evaluate the wet weight of A. japonicus, using machine vision technology combined with a support vector machine (SVM) that can be used in field surveys on the A. japonicus population. Continuous dorsal images of free-moving A. japonicus individuals in seawater were captured, which also allows for the development of images of the core body edge as well as thorn segmentation. Parameters that include body length, body breadth, perimeter and area, were extracted from the core body edge images and used in SVM regression, to predict the weight of A. japonicus and for comparison with a power model. Results indicate that the use of SVM for predicting the weight of 33 A. japonicus individuals is accurate ( R 2=0.99) and compatible with the power model ( R 2 =0.96). The image-based analysis and size-weight regression models in this study may be useful in body weight evaluation of A. japonicus in lab and field study.

  11. Longitudinal impact of weight misperception and intent to change weight on body mass index of adolescents and young adults with overweight or obesity.

    Science.gov (United States)

    Rancourt, Diana; Thurston, Idia B; Sonneville, Kendrin R; Milliren, Carly E; Richmond, Tracy K

    2017-12-01

    Accurate perception of one's weight status is believed to be necessary to motivate weight loss intention and subsequent weight loss among those with overweight/obesity. This proposed pathway, however, is understudied in longitudinal research. This study examined the indirect effect of weight change intention on the relationship between weight status perception and BMI change among adolescents with overweight/obesity. Participants included 2664 adolescents with overweight/obesity (52% female) from the National Longitudinal Study of Adolescent Health. Longitudinal associations between Wave II weight status perception (accurate versus misperception) and intent to change weight (i.e., gain, lose, stay the same) on BMI change (Wave II-Wave IV) were examined using multiple linear regression. Indirect effects of weight change intention were investigated using the Monte Carlo method. Analyses were stratified by gender. Accurate perceivers (81.0% female; 60.1% male) were more likely than misperceivers (i.e., perception of "about the right weight") to report weight loss intention (p<0.001). Among females, weight status misperception and weight loss intention individually were associated with smaller (β=-1.37, 95% CI [-2.64, -0.10]) and greater (β=1.18, 95% CI [0.11, 2.25]) BMI gains, respectively. Among males, fully adjusted models suggested that weight status misperception was associated with significantly smaller gains in BMI over time (β=-1.51, 95% CI [-2.38, -0.63]). Weight change intention did not emerge as an indirect effect for either gender. Although weight status misperception was protective against weight gain, weight change intention did not provide an explanation for this relationship. Copyright © 2017 Elsevier Ltd. All rights reserved.

  12. Inverse odds ratio-weighted estimation for causal mediation analysis.

    Science.gov (United States)

    Tchetgen Tchetgen, Eric J

    2013-11-20

    An important scientific goal of studies in the health and social sciences is increasingly to determine to what extent the total effect of a point exposure is mediated by an intermediate variable on the causal pathway between the exposure and the outcome. A causal framework has recently been proposed for mediation analysis, which gives rise to new definitions, formal identification results and novel estimators of direct and indirect effects. In the present paper, the author describes a new inverse odds ratio-weighted approach to estimate so-called natural direct and indirect effects. The approach, which uses as a weight the inverse of an estimate of the odds ratio function relating the exposure and the mediator, is universal in that it can be used to decompose total effects in a number of regression models commonly used in practice. Specifically, the approach may be used for effect decomposition in generalized linear models with a nonlinear link function, and in a number of other commonly used models such as the Cox proportional hazards regression for a survival outcome. The approach is simple and can be implemented in standard software provided a weight can be specified for each observation. An additional advantage of the method is that it easily incorporates multiple mediators of a categorical, discrete or continuous nature. Copyright © 2013 John Wiley & Sons, Ltd.

  13. Thyroid Function and Body Weight: A Community-Based Longitudinal Study

    DEFF Research Database (Denmark)

    Bjergved, Lena; Jørgensen, Torben; Perrild, Hans

    2014-01-01

    . Weight increased by 0.3 kg (95% confidence interval [CI] 0.1, 0.4, P = 0.005) in women and 0.8 kg (95% CI 0.1, 1.4, P = 0.02) in men for every one unit TSH (mU/L) increase. Conclusions: TSH levels were not a determinant of future weight changes, and BMI was not a determinant for TSH changes......,102 individuals who participated at 11-year follow-up, without current or former treatment for thyroid disease and with measurements of TSH and weight at both examinations. Multiple linear regression models were used, stratified by sex and adjusted for age, smoking status, and leisure time physical activity....... Results: Baseline TSH concentration was not associated with change in weight (women, P = 0.17; men, P = 0.72), and baseline body mass index (BMI) was not associated with change in TSH (women, P = 0.21; men, P = 0.85). Change in serum TSH and change in weight were significantly associated in both sexes...

  14. Estimation of Relative Economic Weights of Hanwoo Carcass Traits Based on Carcass Market Price

    Science.gov (United States)

    Choy, Yun Ho; Park, Byoung Ho; Choi, Tae Jung; Choi, Jae Gwan; Cho, Kwang Hyun; Lee, Seung Soo; Choi, You Lim; Koh, Kyung Chul; Kim, Hyo Sun

    2012-01-01

    The objective of this study was to estimate economic weights of Hanwoo carcass traits that can be used to build economic selection indexes for selection of seedstocks. Data from carcass measures for determining beef yield and quality grades were collected and provided by the Korean Institute for Animal Products Quality Evaluation (KAPE). Out of 1,556,971 records, 476,430 records collected from 13 abattoirs from 2008 to 2010 after deletion of outlying observations were used to estimate relative economic weights of bid price per kg carcass weight on cold carcass weight (CW), eye muscle area (EMA), backfat thickness (BF) and marbling score (MS) and the phenotypic relationships among component traits. Price of carcass tended to increase linearly as yield grades or quality grades, in marginal or in combination, increased. Partial regression coefficients for MS, EMA, BF, and for CW in original scales were +948.5 won/score, +27.3 won/cm2, −95.2 won/mm and +7.3 won/kg when all three sex categories were taken into account. Among four grade determining traits, relative economic weight of MS was the greatest. Variations in partial regression coefficients by sex categories were great but the trends in relative weights for each carcass measures were similar. Relative economic weights of four traits in integer values when standardized measures were fit into covariance model were +4:+1:−1:+1 for MS:EMA:BF:CW. Further research is required to account for the cost of production per unit carcass weight or per unit production under different economic situations. PMID:25049531

  15. The detection of influential subsets in linear regression using an influence matrix

    OpenAIRE

    Peña, Daniel; Yohai, Víctor J.

    1991-01-01

    This paper presents a new method to identify influential subsets in linear regression problems. The procedure uses the eigenstructure of an influence matrix which is defined as the matrix of uncentered covariance of the effect on the whole data set of deleting each observation, normalized to include the univariate Cook's statistics in the diagonal. It is shown that points in an influential subset will appear with large weight in at least one of the eigenvector linked to the largest eigenvalue...

  16. Is low birth weight a risk factor for asthma during adolescence?

    OpenAIRE

    Seidman, D S; Laor, A; Gale, R; Stevenson, D K; Danon, Y L

    1991-01-01

    The effect of low birth weight on the incidence of asthma by 17 years of age was investigated by studying medical draft examination records of 20,312 male subjects born in Jerusalem between January 1967 and December 1971. Additional information on birth weight and other demographic factors was abstracted from the Jerusalem Perinatal Study computerised database. A stepwise multiple logistic regression was used to estimate the odds ratios for developing asthma by 17 years of age in 500 g birthw...

  17. Robust frameless stereotactic localization in extra-cranial radiotherapy

    International Nuclear Information System (INIS)

    Riboldi, Marco; Baroni, Guido; Spadea, Maria Francesca; Bassanini, Fabio; Tagaste, Barbara; Garibaldi, Cristina; Orecchia, Roberto; Pedotti, Antonio

    2006-01-01

    In the field of extra-cranial radiotherapy, several inaccuracies can make the application of frameless stereotactic localization techniques error-prone. When optical tracking systems based on surface fiducials are used, inter- and intra-fractional uncertainties in marker three-dimensional (3D) detection may lead to inexact tumor position estimation, resulting in erroneous patient setup. This is due to the fact that external fiducials misdetection results in deformation effects that are poorly handled in a rigid-body approach. In this work, the performance of two frameless stereotactic localization algorithms for 3D tumor position reconstruction in extra-cranial radiotherapy has been specifically tested. Two strategies, unweighted versus weighted, for stereotactic tumor localization were examined by exploiting data coming from 46 patients treated for extra-cranial lesions. Measured isocenter displacements and rotations were combined to define isocentric procedures, featuring 6 degrees of freedom, for correcting patient alignment (isocentric positioning correction). The sensitivity of the algorithms to uncertainties in the 3D localization of fiducials was investigated by means of 184 numerical simulations. The performance of the implemented isocentric positioning correction was compared to conventional point-based registration. The isocentric positioning correction algorithm was tested on a clinical dataset of inter-fractional and intra-fractional setup errors, which was collected by means of an optical tracker on the same group of patients. The weighted strategy exhibited a lower sensitivity to fiducial localization errors in simulated misalignments than those of the unweighted strategy. Isocenter 3D displacements provided by the weighted strategy were consistently smaller than those featured by the unweighted strategy. The peak decrease in median and quartile values of isocenter 3D displacements were 1.4 and 2.7 mm, respectively. Concerning clinical data, the

  18. Effects of social contact and zygosity on 21-y weight change in male twins.

    Science.gov (United States)

    McCaffery, Jeanne M; Franz, Carol E; Jacobson, Kristen; Leahey, Tricia M; Xian, Hong; Wing, Rena R; Lyons, Michael J; Kremen, William S

    2011-08-01

    Recent evidence indicates that social contact is related to similarities in weight gain over time. However, no studies have examined this effect in a twin design, in which genetic and other environmental effects can also be estimated. We determined whether the frequency of social contact is associated with similarity in weight change from young adulthood (mean age: 20 y) to middle age (mean age: 41 y) in twins and quantified the percentage of variance in weight change attributable to social contact, genetic factors, and other environmental influences. Participants were 1966 monozygotic and 1529 dizygotic male twin pairs from the Vietnam-Era Twin Registry. Regression models tested whether frequency of social contact and zygosity predicted twin pair similarity in body mass index (BMI) change and weight change. Twin modeling was used to partition the percentage variance attributable to social contact, genetic, and other environmental effects. Twins gained an average of 3.99 BMI units, or 13.23 kg (29.11 lb), over 21 y. In regression models, both zygosity (P social contact (P change. In twin modeling, social contact between twins contributed 16% of the variance in BMI change (P change. Frequency of social contact significantly predicted twin pair similarity in BMI and weight change over 21 y, independent of zygosity and other shared environmental influences.

  19. Association between blood cholesterol and sodium intake in hypertensive women with excess weight.

    Science.gov (United States)

    Padilha, Bruna Merten; Ferreira, Raphaela Costa; Bueno, Nassib Bezerra; Tassitano, Rafael Miranda; Holanda, Lidiana de Souza; Vasconcelos, Sandra Mary Lima; Cabral, Poliana Coelho

    2018-04-01

    Restricted sodium intake has been recommended for more than 1 century for the treatment of hypertension. However, restriction seems to increase blood cholesterol. In women with excess weight, blood cholesterol may increase even more because of insulin resistance and the high lipolytic activity of adipose tissue.The aim of this study was to assess the association between blood cholesterol and sodium intake in hypertensive women with and without excess weight.This was a cross-sectional study with hypertensive and nondiabetic women aged 20 to 59 years, recruited at the primary healthcare units of Maceio, Alagoas, Brazilian Northeast. Excess weight was defined as body mass index (BMI) ≥25.0 kg/m. Sodium intake was estimated by the 24-hour urinary excretion of sodium. Blood cholesterol was the primary outcome investigated by this study, and its relationship with sodium intake and other variables was assessed by Pearson correlation and multivariate linear regression using a significance level of 5%.This study included 165 hypertensive women. Of these, 135 (81.8%) were with excess weight. The mean sodium intake was 3.7 g (±1.9) and 3.4 g (±2.4) in hypertensive women with and without excess weight, respectively. The multiple normal linear regression models fitted to the "blood cholesterol" in the 2 groups reveal that for the group of hypertensive women without excess weight only 1 independent variable "age" is statistically significant to explain the variability of the blood cholesterol levels. However, for the group of hypertensive women with excess weight, 2 independent variables, age and sodium intake, can statistically explain variations of the blood cholesterol levels.Blood cholesterol is statistically inversely related to sodium intake for hypertensive women with excess weight, but it is not statistically related to sodium intake for hypertensive women without excess weight.

  20. Local Spatial Analysis and Dynamic Simulation of Childhood Obesity and Neighbourhood Walkability in a Major Canadian City.

    Science.gov (United States)

    Shahid, Rizwan; Bertazzon, Stefania

    2015-01-01

    Body weight is an important indicator of current and future health and it is even more critical in children, who are tomorrow's adults. This paper analyzes the relationship between childhood obesity and neighbourhood walkability in Calgary, Canada. A multivariate analytical framework recognizes that childhood obesity is also associated with many factors, including socioeconomic status, foodscapes, and environmental factors, as well as less measurable factors, such as individual preferences, that could not be included in this analysis. In contrast with more conventional global analysis, this research employs localized analysis and assesses need-based interventions. The one-size-fit-all strategy may not effectively control obesity rates, since each neighbourhood has unique characteristics that need to be addressed individually. This paper presents an innovative framework combining local analysis with simulation modeling to analyze childhood obesity. Spatial models generally do not deal with simulation over time, making it cumbersome for health planners and policy makers to effectively design and implement interventions and to quantify their impact over time. This research fills this gap by integrating geographically weighted regression (GWR), which identifies vulnerable neighbourhoods and critical factors for childhood obesity, with simulation modeling, which evaluates the impact of the suggested interventions on the targeted neighbourhoods. Neighbourhood walkability was chosen as a potential target for localized interventions, owing to the crucial role of walking in developing a healthy lifestyle, as well as because increasing walkability is relatively more feasible and less expensive then modifying other factors, such as income. Simulation results suggest that local walkability interventions can achieve measurable declines in childhood obesity rates. The results are encouraging, as improvements are likely to compound over time. The results demonstrate that the