Determination of riverbank erosion probability using Locally Weighted Logistic Regression
Ioannidou, Elena; Flori, Aikaterini; Varouchakis, Emmanouil A.; Giannakis, Georgios; Vozinaki, Anthi Eirini K.; Karatzas, George P.; Nikolaidis, Nikolaos
2015-04-01
Riverbank erosion is a natural geomorphologic process that affects the fluvial environment. The most important issue concerning riverbank erosion is the identification of the vulnerable locations. An alternative to the usual hydrodynamic models to predict vulnerable locations is to quantify the probability of erosion occurrence. This can be achieved by identifying the underlying relations between riverbank erosion and the geomorphological or hydrological variables that prevent or stimulate erosion. Thus, riverbank erosion can be determined by a regression model using independent variables that are considered to affect the erosion process. The impact of such variables may vary spatially, therefore, a non-stationary regression model is preferred instead of a stationary equivalent. Locally Weighted Regression (LWR) is proposed as a suitable choice. This method can be extended to predict the binary presence or absence of erosion based on a series of independent local variables by using the logistic regression model. It is referred to as Locally Weighted Logistic Regression (LWLR). Logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (e.g. binary response) based on one or more predictor variables. The method can be combined with LWR to assign weights to local independent variables of the dependent one. LWR allows model parameters to vary over space in order to reflect spatial heterogeneity. The probabilities of the possible outcomes are modelled as a function of the independent variables using a logistic function. Logistic regression measures the relationship between a categorical dependent variable and, usually, one or several continuous independent variables by converting the dependent variable to probability scores. Then, a logistic regression is formed, which predicts success or failure of a given binary variable (e.g. erosion presence or absence) for any value of the independent variables. The
Wheeler, David; Tiefelsdorf, Michael
2005-06-01
Present methodological research on geographically weighted regression (GWR) focuses primarily on extensions of the basic GWR model, while ignoring well-established diagnostics tests commonly used in standard global regression analysis. This paper investigates multicollinearity issues surrounding the local GWR coefficients at a single location and the overall correlation between GWR coefficients associated with two different exogenous variables. Results indicate that the local regression coefficients are potentially collinear even if the underlying exogenous variables in the data generating process are uncorrelated. Based on these findings, applied GWR research should practice caution in substantively interpreting the spatial patterns of local GWR coefficients. An empirical disease-mapping example is used to motivate the GWR multicollinearity problem. Controlled experiments are performed to systematically explore coefficient dependency issues in GWR. These experiments specify global models that use eigenvectors from a spatial link matrix as exogenous variables.
STATISTICAL INFERENCES FOR VARYING-COEFFICINT MODELS BASED ON LOCALLY WEIGHTED REGRESSION TECHNIQUE
Institute of Scientific and Technical Information of China (English)
梅长林; 张文修; 梁怡
2001-01-01
Some fundamental issues on statistical inferences relating to varying-coefficient regression models are addressed and studied. An exact testing procedure is proposed for checking the goodness of fit of a varying-coefficient model fired by the locally weighted regression technique versus an ordinary linear regression model. Also, an appropriate statistic for testing variation of model parameters over the locations where the observations are collected is constructed and a formal testing approach which is essential to exploring spatial non-stationarity in geography science is suggested.
Malik, Bilal; Benaissa, Mohammed
2012-01-01
This paper proposes the use of locally weighted partial least square regression (LW-PLSR) as an alternative multivariate calibration method for the prediction of glucose concentration from NIR spectra. The efficiency of the proposed model is validated in experiments carried out in a non-controlled environment or sample conditions using mixtures composed of glucose, urea and triacetin. The collected data spans the spectral region from 2100 nm to 2400 nm with spectra resolution of 1 nm. The results show that the standard error of prediction (SEP) decreases to 23.85 mg/dL when using LW-PLSR in comparison to the SEP values of 49.40 mg/dL, and 27.56 mg/dL using Principal Component Regression (PCR) and Partial Least Square (PLS) regression respectively.
Directory of Open Access Journals (Sweden)
A.Muthukumaravel
2011-08-01
Full Text Available This paper presents implementation of locally weighted projection regression (LWPR network method for concurrency control while developing dial of a fork using Autodesk inventor 2008. The LWPR learns the objects and the type of transactions to be done based on which node in the output layer of the network exceeds a threshold value. Learning stops once all the objects are exposed to LWPR. During testing performance, metrics are analyzed. We have attempted to use LWPR for storing lock information when multi users are working on computer Aided Design (CAD. The memory requirements of the proposed method are minimal in processing locks during transaction.
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
Estrada-Piedra, T; Terlevich, R J; Fuentes, O; Terlevich, E; Estrada-Piedra, Trilce; Torres-Papaqui, Juan Pablo; Terlevich, Roberto; Fuentes, Olac; Terlevich, Elena
2003-01-01
We present a new technique to segregate old and young stellar populations in galactic spectra using machine learning methods. We used an ensemble of classifiers, each classifier in the ensemble specializes in young or old populations and was trained with locally weighted regression and tested using ten-fold cross-validation. Since the relevant information concentrates in certain regions of the spectra we used the method of sequential floating backward selection offline for feature selection. The application to Seyfert galaxies proved that this technique is very insensitive to the dilution by the Active Galactic Nucleus (AGN) continuum. Comparing with exhaustive search we concluded that both methods are similar in terms of accuracy but the machine learning method is faster by about two orders of magnitude.
Arakaki, Lorilee S L; Schenkman, Kenneth A; Ciesielski, Wayne A; Shaver, Jeremy M
2013-06-27
We have developed a method to make real-time, continuous, noninvasive measurements of muscle oxygenation (Mox) from the surface of the skin. A key development was measurement in both the visible and near infrared (NIR) regions. Measurement of both oxygenated and deoxygenated myoglobin and hemoglobin resulted in a more accurate measurement of Mox than could be achieved with measurement of only the deoxygenated components, as in traditional near-infrared spectroscopy (NIRS). Using the second derivative with respect to wavelength reduced the effects of scattering on the spectra and also made oxygenated and deoxygenated forms more distinguishable from each other. Selecting spectral bands where oxygenated and deoxygenated forms absorb filtered out noise and spectral features unrelated to Mox. NIR and visible bands were scaled relative to each other in order to correct for errors introduced by normalization. Multivariate Curve Resolution (MCR) was used to estimate Mox from spectra within each data set collected from healthy subjects. A Locally Weighted Regression (LWR) model was built from calibration set spectra and associated Mox values from 20 subjects using 2562 spectra. LWR and Partial Least Squares (PLS) allow accurate measurement of Mox despite variations in skin pigment or fat layer thickness in different subjects. The method estimated Mox in five healthy subjects with an RMSE of 5.4%.
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...... 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...
Clary, Christelle; Lewis, Daniel J; Flint, Ellen; Smith, Neil R; Kestens, Yan; Cummins, Steven
2016-12-01
Studies that explore associations between the local food environment and diet routinely use global regression models, which assume that relationships are invariant across space, yet such stationarity assumptions have been little tested. We used global and geographically weighted regression models to explore associations between the residential food environment and fruit and vegetable intake. Analyses were performed in 4 boroughs of London, United Kingdom, using data collected between April 2012 and July 2012 from 969 adults in the Olympic Regeneration in East London Study. Exposures were assessed both as absolute densities of healthy and unhealthy outlets, taken separately, and as a relative measure (proportion of total outlets classified as healthy). Overall, local models performed better than global models (lower Akaike information criterion). Locally estimated coefficients varied across space, regardless of the type of exposure measure, although changes of sign were observed only when absolute measures were used. Despite findings from global models showing significant associations between the relative measure and fruit and vegetable intake (β = 0.022; P environment and diet. It further challenges the idea that a single measure of exposure, whether relative or absolute, can reflect the many ways the food environment may shape health behaviors. © The Author 2016. 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.
Fungible weights in logistic regression.
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
Correlation Weights in Multiple Regression
Waller, Niels G.; Jones, Jeff A.
2010-01-01
A general theory on the use of correlation weights in linear prediction has yet to be proposed. In this paper we take initial steps in developing such a theory by describing the conditions under which correlation weights perform well in population regression models. Using OLS weights as a comparison, we define cases in which the two weighting…
Harrison, R. J.; Feinberg, J. M.
2007-12-01
First-order reversal curves (FORCs) are a powerful method for characterizing the magnetic hysteresis properties of natural and synthetic materials, and are rapidly becoming a standard tool in rock magnetic and paleomagnetic investigations. Here we describe a modification to existing algorithms for the calculation of FORC diagrams using locally-weighted regression smoothing (often referred to as loess smoothing). Like conventional algorithms, the FORC distribution is calculated by fitting a second degree polynomial to a region of FORC space defined by a smoothing factor, N. Our method differs from conventional algorithms in two ways. Firstly, rather than a square of side (2N+1) centered on the point of interest, the region of FORC space used for fitting is defined as a span of arbitrary shape encompassing the (2N+1)2 data points closest to the point of interest. Secondly, data inside the span are given a weight that depends on their distance from the point being evaluated: data closer to the point being evaluated have higher weights and have a greater effect on the fit. Loess smoothing offers two advantages over current methods. Firstly, it allows the FORC distribution to be calculated using a constant smoothing factor all the way to the Hc = 0 axis. This eliminates possible distortions to the FORC distribution associated with reducing the smoothing factor close to the Hc = 0 axis, and does not require use of the extended FORC formalism and the reversible ridge, which swamps the low-coercivity signal. Secondly, it allows finer control over the degree of smoothing applied to the data, enabling automated selection of the optimum smoothing factor for a given FORC measurement, based on an analysis of the standard deviation of the fit residuals. The new algorithm forms the basis for FORCinel, a new suite of FORC analysis tools for Igor Pro (www.wavemetrics.com), freely available on request from the authors.
Geographically weighted regression and multicollinearity: dispelling the myth
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.
Approximation by randomly weighting method in censored regression model
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
Censored regression ("Tobit") models have been in common use, and their linear hypothesis testings have been widely studied. However, the critical values of these tests are usually related to quantities of an unknown error distribution and estimators of nuisance parameters. In this paper, we propose a randomly weighting test statistic and take its conditional distribution as an approximation to null distribution of the test statistic. It is shown that, under both the null and local alternative hypotheses, conditionally asymptotic distribution of the randomly weighting test statistic is the same as the null distribution of the test statistic. Therefore, the critical values of the test statistic can be obtained by randomly weighting method without estimating the nuisance parameters. At the same time, we also achieve the weak consistency and asymptotic normality of the randomly weighting least absolute deviation estimate in censored regression model. Simulation studies illustrate that the per-formance of our proposed resampling test method is better than that of central chi-square distribution under the null hypothesis.
Approximation by randomly weighting method in censored regression model
Institute of Scientific and Technical Information of China (English)
WANG ZhanFeng; WU YaoHua; ZHAO LinCheng
2009-01-01
Censored regression ("Tobit") models have been in common use,and their linear hypothesis testings have been widely studied.However,the critical values of these tests are usually related to quantities of an unknown error distribution and estimators of nuisance parameters.In this paper,we propose a randomly weighting test statistic and take its conditional distribution as an approximation to null distribution of the test statistic.It is shown that,under both the null and local alternative hypotheses,conditionally asymptotic distribution of the randomly weighting test statistic is the same as the null distribution of the test statistic.Therefore,the critical values of the test statistic can be obtained by randomly weighting method without estimating the nuisance parameters.At the same time,we also achieve the weak consistency and asymptotic normality of the randomly weighting least absolute deviation estimate in censored regression model.Simulation studies illustrate that the performance of our proposed resampling test method is better than that of central chi-square distribution under the null hypothesis.
Penalized Weighted Least Squares for Outlier Detection and Robust Regression
Gao, Xiaoli; Fang, Yixin
2016-01-01
To conduct regression analysis for data contaminated with outliers, many approaches have been proposed for simultaneous outlier detection and robust regression, so is the approach proposed in this manuscript. This new approach is called "penalized weighted least squares" (PWLS). By assigning each observation an individual weight and incorporating a lasso-type penalty on the log-transformation of the weight vector, the PWLS is able to perform outlier detection and robust regression simultaneou...
Alcohol outlet density and violence: A geographically weighted regression approach.
Cameron, Michael P; Cochrane, William; Gordon, Craig; Livingston, Michael
2016-05-01
We investigate the relationship between outlet density (of different types) and violence (as measured by police activity) across the North Island of New Zealand, specifically looking at whether the relationships vary spatially. We use New Zealand data at the census area unit (approximately suburb) level, on police-attended violent incidents and outlet density (by type of outlet), controlling for population density and local social deprivation. We employed geographically weighted regression to obtain both global average and locally specific estimates of the relationships between alcohol outlet density and violence. We find that bar and night club density, and licensed club density (e.g. sports clubs) have statistically significant and positive relationships with violence, with an additional bar or night club is associated with nearly 5.3 additional violent events per year, and an additional licensed club associated with 0.8 additional violent events per year. These relationships do not show significant spatial variation. In contrast, the effects of off-licence density and restaurant/café density do exhibit significant spatial variation. However, the non-varying effects of bar and night club density are larger than the locally specific effects of other outlet types. The relationships between outlet density and violence vary significantly across space for off-licences and restaurants/cafés. These results suggest that in order to minimise alcohol-related harms, such as violence, locally specific policy interventions are likely to be necessary. [Cameron MP, Cochrane W, Gordon C, Livingston M. Alcohol outlet density and violence: A geographically weighted regression approach. Drug Alcohol Rev 2016;35:280-288]. © 2015 Australasian Professional Society on Alcohol and other Drugs.
Robust Depth-Weighted Wavelet for Nonparametric Regression Models
Institute of Scientific and Technical Information of China (English)
Lu LIN
2005-01-01
In the nonpaxametric regression models, the original regression estimators including kernel estimator, Fourier series estimator and wavelet estimator are always constructed by the weighted sum of data, and the weights depend only on the distance between the design points and estimation points. As a result these estimators are not robust to the perturbations in data. In order to avoid this problem, a new nonparametric regression model, called the depth-weighted regression model, is introduced and then the depth-weighted wavelet estimation is defined. The new estimation is robust to the perturbations in data, which attains very high breakdown value close to 1/2. On the other hand, some asymptotic behaviours such as asymptotic normality are obtained. Some simulations illustrate that the proposed wavelet estimator is more robust than the original wavelet estimator and, as a price to pay for the robustness, the new method is slightly less efficient than the original method.
Use of probabilistic weights to enhance linear regression myoelectric control
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 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.
Use of probabilistic weights to enhance linear regression myoelectric control.
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.
Afifah, Rawyanil; Andriyana, Yudhie; Jaya, I. G. N. Mindra
2017-03-01
Geographically Weighted Regression (GWR) is a development of an Ordinary Least Squares (OLS) regression which is quite effective in estimating spatial non-stationary data. On the GWR models, regression parameters are generated locally, each observation has a unique regression coefficient. Parameter estimation process in GWR uses Weighted Least Squares (WLS). But when there are outliers in the data, the parameter estimation process with WLS produces estimators which are not efficient. Hence, this study uses a robust method called Least Absolute Deviation (LAD), to estimate the parameters of GWR model in the case of poverty in Java Island. This study concludes that GWR model with LAD method has a better performance.
Neither fixed nor random: weighted least squares meta-regression.
Stanley, T D; Doucouliagos, Hristos
2016-06-20
Our study revisits and challenges two core conventional meta-regression estimators: the prevalent use of 'mixed-effects' or random-effects meta-regression analysis and the correction of standard errors that defines fixed-effects meta-regression analysis (FE-MRA). We show how and explain why an unrestricted weighted least squares MRA (WLS-MRA) estimator is superior to conventional random-effects (or mixed-effects) meta-regression when there is publication (or small-sample) bias that is as good as FE-MRA in all cases and better than fixed effects in most practical applications. Simulations and statistical theory show that WLS-MRA provides satisfactory estimates of meta-regression coefficients that are practically equivalent to mixed effects or random effects when there is no publication bias. When there is publication selection bias, WLS-MRA always has smaller bias than mixed effects or random effects. In practical applications, an unrestricted WLS meta-regression is likely to give practically equivalent or superior estimates to fixed-effects, random-effects, and mixed-effects meta-regression approaches. However, random-effects meta-regression remains viable and perhaps somewhat preferable if selection for statistical significance (publication bias) can be ruled out and when random, additive normal heterogeneity is known to directly affect the 'true' regression coefficient. Copyright © 2016 John Wiley & Sons, Ltd.
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model
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.
Cluster-localized sparse logistic regression for SNP data.
Binder, Harald; Müller, Tina; Schwender, Holger; Golka, Klaus; Steffens, Michael; Hengstler, Jan G; Ickstadt, Katja; Schumacher, Martin
2012-08-14
The task of analyzing high-dimensional single nucleotide polymorphism (SNP) data in a case-control design using multivariable techniques has only recently been tackled. While many available approaches investigate only main effects in a high-dimensional setting, we propose a more flexible technique, cluster-localized regression (CLR), based on localized logistic regression models, that allows different SNPs to have an effect for different groups of individuals. Separate multivariable regression models are fitted for the different groups of individuals by incorporating weights into componentwise boosting, which provides simultaneous variable selection, hence sparse fits. For model fitting, these groups of individuals are identified using a clustering approach, where each group may be defined via different SNPs. This allows for representing complex interaction patterns, such as compositional epistasis, that might not be detected by a single main effects model. In a simulation study, the CLR approach results in improved prediction performance, compared to the main effects approach, and identification of important SNPs in several scenarios. Improved prediction performance is also obtained for an application example considering urinary bladder cancer. Some of the identified SNPs are predictive for all individuals, while others are only relevant for a specific group. Together with the sets of SNPs that define the groups, potential interaction patterns are uncovered.
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.
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.
Local Linear Regression for Data with AR Errors
Institute of Scientific and Technical Information of China (English)
Runze Li; Yan Li
2009-01-01
In many statistical applications, data are collected over time, and they are likely correlated. In this paper, we investigate how to incorporate the correlation information into the local linear regression. Under the assumption that the error process is an auto-regressive process, a new estimation procedure is proposed for the nonparametric regression by using local linear regression method and the profile least squares techniques.We further propose the SCAD penalized profile least squares method to determine the order of auto-regressive process. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed procedure, and to compare the performance of the proposed procedures with the existing one.From our empirical studies, the newly proposed procedures can dramatically improve the accuracy of naive local linear regression with working-independent error structure. We illustrate the proposed methodology by an analysis of real data set.
Yang, Shun-hua; Zhang, Hai-tao; Guo, Long; Ren, Yan
2015-06-01
Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control. The results indicated that soil or- ganic matter was significantly positively correlated with relative elevation whilst it had a significantly negative correlation with stream power index. Semivariance analysis showed that both soil organic matter content and its residuals (including ordinary least square regression residual and GWR resi- dual) had strong spatial autocorrelation. Interpolation accuracies by different methods were esti- mated based on a data set of 98 validation samples. Results showed that the mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) of RK were respectively 39.2%, 17.7% and 20.6% lower than the corresponding values of OK, with a relative-improvement (RI) of 20.63. GWRK showed a similar tendency, having its ME, MAE and RMSE to be respectively 60.6%, 23.7% and 27.6% lower than those of OK, with a RI of 59.79. Therefore, both RK and GWRK significantly improved the accuracy of OK interpolation of soil organic matter due to their in- corporation of auxiliary variables. In addition, GWRK performed obviously better than RK did in this study, and its improved performance should be attributed to the consideration of sample spatial locations.
An Efficient Local Algorithm for Distributed Multivariate Regression
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...
A Scalable Local Algorithm for Distributed Multivariate Regression
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...
Institute of Scientific and Technical Information of China (English)
2009-01-01
In this paper, we study the local asymptotic behavior of the regression spline estimator in the framework of marginal semiparametric model. Similarly to Zhu, Fung and He (2008), we give explicit expression for the asymptotic bias of regression spline estimator for nonparametric function f. Our results also show that the asymptotic bias of the regression spline estimator does not depend on the working covariance matrix, which distinguishes the regression splines from the smoothing splines and the seemingly unrelated kernel. To understand the local bias result of the regression spline estimator, we show that the regression spline estimator can be obtained iteratively by applying the standard weighted least squares regression spline estimator to pseudo-observations. At each iteration, the bias of the estimator is unchanged and only the variance is updated.
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.
[Regression of Morton neuroma after local injection of steroids].
Haddad-Zebouni, S; Elia, D; Aoun, N; Okais, J; Ghossain, M
2006-05-01
Morton neuroma is a non neoplastic lesion corresponding to perineural fibrosis encircling the common interdigital plantar nerve. Several therapeutic approaches are possible: conservative treatment or surgery. We report a case treated by local steroid injection where follow-up MR showed near complete regression of the lesion. Although local injection of steroid is a classical treatment, it is the first time to our knowledge that resolution or such a striking diminution of size is reported after infiltration.
Directory of Open Access Journals (Sweden)
M ARRIE KUNILASARI ELYNA
2012-09-01
Full Text Available Alpha In this study the used method of Geographically Weighted Poisson Regression (GWPR is a statistical method to analyze the data to account for spatial factors. GWPR is a local form of Poisson regression with respect to the location of the assumption that the data is Poisson distributed. There are factors that are used in this study is the number of health facilities and midwives, the average length of breastfeeding, the percentage of deliveries performed by non-medical assistance, and the average length of schooling a woman is married. The research results showed that factors significantly influence the number of infant deaths in sluruh districts / municipalities in Bali is the average length of schooling a woman is married. Then the results of hypothesis test obtained the results that there was no difference who significant between the regression model poisson and GWPR in Bali.
Using Weighted Least Squares Regression for Obtaining Langmuir Sorption Constants
One of the most commonly used models for describing phosphorus (P) sorption to soils is the Langmuir model. To obtain model parameters, the Langmuir model is fit to measured sorption data using least squares regression. Least squares regression is based on several assumptions including normally dist...
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...
Access to Local Agriculture and Weight Outcomes
Joshua P. Berning
2012-01-01
Recent studies examine the impact of the built environment on health outcomes such as obesity. Several studies find for certain populations that access to unhealthy food has a positive effect on obesity, whereas access to healthy choices has a negative effect. Given the growth and popularity of locally grown food, we examine how individual weight outcomes are affected by access to direct-to-consumer local food. After controlling for potential endogeneity, we find that greater access to local ...
Local Linear Regression on Manifolds and its Geometric Interpretation
Cheng, Ming-Yen
2012-01-01
We study nonparametric regression with high-dimensional data, when the predictors lie on an unknown, lower-dimensional manifold. In this context, recently \\cite{aswani_bickel:2011} suggested performing the conventional local linear regression (LLR) in the ambient space and regularizing the estimation problem using information obtained from learning the manifold locally. By contrast, our approach is to reduce the dimensionality first and then construct the LLR directly on a tangent plane approximation to the manifold. Under mild conditions, asymptotic expressions for the conditional mean squared error of the proposed estimator are derived for both the interior and the boundary cases. One implication of these results is that the optimal convergence rate depends only on the intrinsic dimension $d$ of the manifold, but not on the ambient space dimension $p$. Another implication is that the estimator is design adaptive and automatically adapts to the boundary of the unknown manifold. The bias and variance expressi...
Power Prediction in Smart Grids with Evolutionary Local Kernel Regression
Kramer, Oliver; Satzger, Benjamin; Lässig, Jörg
Electric grids are moving from a centralized single supply chain towards a decentralized bidirectional grid of suppliers and consumers in an uncertain and dynamic scenario. Soon, the growing smart meter infrastructure will allow the collection of terabytes of detailed data about the grid condition, e.g., the state of renewable electric energy producers or the power consumption of millions of private customers, in very short time steps. For reliable prediction strong and fast regression methods are necessary that are able to cope with these challenges. In this paper we introduce a novel regression technique, i.e., evolutionary local kernel regression, a kernel regression variant based on local Nadaraya-Watson estimators with independent bandwidths distributed in data space. The model is regularized with the CMA-ES, a stochastic non-convex optimization method. We experimentally analyze the load forecast behavior on real power consumption data. The proposed method is easily parallelizable, and therefore well appropriate for large-scale scenarios in smart grids.
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....
Inverse probability weighted Cox regression for doubly truncated data.
Mandel, Micha; de Uña-Álvarez, Jacobo; Simon, David K; Betensky, Rebecca A
2017-09-08
Doubly truncated data arise when event times are observed only if they fall within subject-specific, possibly random, intervals. While non-parametric methods for survivor function estimation using doubly truncated data have been intensively studied, only a few methods for fitting regression models have been suggested, and only for a limited number of covariates. In this article, we present a method to fit the Cox regression model to doubly truncated data with multiple discrete and continuous covariates, and describe how to implement it using existing software. The approach is used to study the association between candidate single nucleotide polymorphisms and age of onset of Parkinson's disease. © 2017, The International Biometric Society.
Estimating monotonic rates from biological data using local linear regression.
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.
Multivariate Local Polynomial Regression with Application to Shenzhen Component Index
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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.
Tong, Fuhui
2006-01-01
Background: An extensive body of researches has favored the use of regression over other parametric analyses that are based on OVA. In case of noteworthy regression results, researchers tend to explore magnitude of beta weights for the respective predictors. Purpose: The purpose of this paper is to examine both beta weights and structure…
Procedures for adjusting regional regression models of urban-runoff quality using local data
Hoos, A.B.; Sisolak, J.K.
1993-01-01
Statistical operations termed model-adjustment procedures (MAP?s) can be used to incorporate local data into existing regression models to improve the prediction of urban-runoff quality. Each MAP is a form of regression analysis in which the local data base is used as a calibration data set. Regression coefficients are determined from the local data base, and the resulting `adjusted? regression models can then be used to predict storm-runoff quality at unmonitored sites. The response variable in the regression analyses is the observed load or mean concentration of a constituent in storm runoff for a single storm. The set of explanatory variables used in the regression analyses is different for each MAP, but always includes the predicted value of load or mean concentration from a regional regression model. The four MAP?s examined in this study were: single-factor regression against the regional model prediction, P, (termed MAP-lF-P), regression against P,, (termed MAP-R-P), regression against P, and additional local variables (termed MAP-R-P+nV), and a weighted combination of P, and a local-regression prediction (termed MAP-W). The procedures were tested by means of split-sample analysis, using data from three cities included in the Nationwide Urban Runoff Program: Denver, Colorado; Bellevue, Washington; and Knoxville, Tennessee. The MAP that provided the greatest predictive accuracy for the verification data set differed among the three test data bases and among model types (MAP-W for Denver and Knoxville, MAP-lF-P and MAP-R-P for Bellevue load models, and MAP-R-P+nV for Bellevue concentration models) and, in many cases, was not clearly indicated by the values of standard error of estimate for the calibration data set. A scheme to guide MAP selection, based on exploratory data analysis of the calibration data set, is presented and tested. The MAP?s were tested for sensitivity to the size of a calibration data set. As expected, predictive accuracy of all MAP?s for
Portfolio optimization using local linear regression ensembles in RapidMiner
Gabor Nagy; Gergo Barta; Tamas Henk
2015-01-01
In this paper we implement a Local Linear Regression Ensemble Committee (LOLREC) to predict 1-day-ahead returns of 453 assets form the S&P500. The estimates and the historical returns of the committees are used to compute the weights of the portfolio from the 453 stock. The proposed method outperforms benchmark portfolio selection strategies that optimize the growth rate of the capital. We investigate the effect of algorithm parameter m: the number of selected stocks on achieved average annua...
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.
Area-to-point parameter estimation with geographically weighted regression
Murakami, Daisuke; Tsutsumi, Morito
2015-07-01
The modifiable areal unit problem (MAUP) is a problem by which aggregated units of data influence the results of spatial data analysis. Standard GWR, which ignores aggregation mechanisms, cannot be considered to serve as an efficient countermeasure of MAUP. Accordingly, this study proposes a type of GWR with aggregation mechanisms, termed area-to-point (ATP) GWR herein. ATP GWR, which is closely related to geostatistical approaches, estimates the disaggregate-level local trend parameters by using aggregated variables. We examine the effectiveness of ATP GWR for mitigating MAUP through a simulation study and an empirical study. The simulation study indicates that the method proposed herein is robust to the MAUP when the spatial scales of aggregation are not too global compared with the scale of the underlying spatial variations. The empirical studies demonstrate that the method provides intuitively consistent estimates.
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Prima Widayani
2017-01-01
Full Text Available Abstract Geographically Weighted Regression (GWR is regression model that developed for data modeling with continuous respond variable and considering the spatial or location aspect. Leptospirosis case happened in some regions in Indonesia, including in Bantul District, Special Region of Yogyakarta. The purpose of this study are to determine local and global variable in making vulnerable area model of Leptospirosis disease, determine the best type of weighting function and make vulnerable area map of Leptospirosis. Alos satelite imagery as primary data to get settlement and paddy fields area. The others variable are the percentage of population’s age, flood risk, and the number of health facility that obtained from secondary data. Determinant variables that affect locally are flood risk, health facility, percentage of age 25-50 years old and the percentage of settlement area. Meanwhile, independent variable that affects globally is the percentage of paddy fields area. Vulnerability map of Leptospirosis disease resulted from the best GWR model which used weighting function Fixed Bisquare. There are 3 vulnerable area of Leptospirosis disease, high vulnerability area located in the middle of Bantul District, meanwhile the medium and low vulnerability area showed clustered pattern in the side of Bantul District. Abstrak Geographically Weighted Regression (GWR adalah model regresi yang dikembangkan untuk memodelkan data dengan variabel respon yang bersifat kontinu dan mempertimbangkan aspek spasial atau lokasi. Kejadian Leptospirosis terjadi di beberapa wilayah di Indonesia termasuk di wilayah Kabupaten Bantul Daerah Istimewa Yogyakarta. Tujuan dari penelitian ini adalah menentukan variabel lokal dan global dalam membuat model kerentanan Leptospirosis dan menentukan jenis fungsi pembobot yang terbaik serta membuat peta kerentanan wilayah Leptospirosis menggunakan aplikasi GWR. Citra Satelit Alos digunakan untuk mendapatkan data penggunaan
Ciupak, Maurycy; Ozga-Zielinski, Bogdan; Adamowski, Jan; Quilty, John; Khalil, Bahaa
2015-11-01
A novel implementation of Dynamic Linear Bayesian Models (DLBM), using either a Varying Coefficient Regression (VCR) or a Discount Weighted Regression (DWR) algorithm was used in the hydrological modeling of annual hydrographs as well as 1-, 2-, and 3-day lead time stream flow forecasting. Using hydrological data (daily discharge, rainfall, and mean, maximum and minimum air temperatures) from the Upper Narew River watershed in Poland, the forecasting performance of DLBM was compared to that of traditional multiple linear regression (MLR) and more recent artificial neural network (ANN) based models. Model performance was ranked DLBM-DWR > DLBM-VCR > MLR > ANN for both annual hydrograph modeling and 1-, 2-, and 3-day lead forecasting, indicating that the DWR and VCR algorithms, operating in a DLBM framework, represent promising new methods for both annual hydrograph modeling and short-term stream flow forecasting.
Geographically weighted regression as a generalized Wombling to detect barriers to gene flow.
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.
On the Relationship Between Confidence Sets and Exchangeable Weights in Multiple Linear Regression.
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.
Genetic parameters for various random regression models to describe the weight data of pigs
Huisman, A.E.; Veerkamp, R.F.; Arendonk, van J.A.M.
2002-01-01
Various random regression models have been advocated for the fitting of covariance structures. It was suggested that a spline model would fit better to weight data than a random regression model that utilizes orthogonal polynomials. The objective of this study was to investigate which kind of random
Genetic parameters for different random regression models to describe weight data of pigs
Huisman, A.E.; Veerkamp, R.F.; Arendonk, van J.A.M.
2001-01-01
Various random regression models have been advocated for the fitting of covariance structures. It was suggested that a spline model would fit better to weight data than a random regression model that utilizes orthogonal polynomials. The objective of this study was to investigate which kind of random
Askelöf, P; Korsfeldt, M; Mannervik, B
1976-10-01
Knowledge of the error structure of a given set of experimental data is a necessary prerequisite for incisive analysis and for discrimination between alternative mathematical models of the data set. A reaction system consisting of glutathione S-transferase A (glutathione S-aryltransferase), glutathione, and 3,4-dichloro-1-nitrobenzene was investigated under steady-state conditions. It was found that the experimental error increased with initial velocity, v, and that the variance (estimated by replicates) could be described by a polynomial in v Var (v) = K0 + K1 - v + K2 - v2 or by a power function Var (v) = K0 + K1 - vK2. These equations were good approximations irrespective of whether different v values were generated by changing substrate or enzyme concentrations. The selection of these models was based mainly on experiments involving varying enzyme concentration, which, unlike v, is not considered a stochastic variable. Different models of the variance, expressed as functions of enzyme concentration, were examined by regression analysis, and the models could then be transformed to functions in which velocity is substituted for enzyme concentration owing to the proportionality between these variables. Thus, neither the absolute nor the relative error was independent of velocity, a result previously obtained for glutathione reductase in this laboratory [BioSystems 7, 101-119 (1975)]. If the experimental errors or velocities were standardized by division with their corresponding mean velocity value they showed a normal (Gaussian) distribution provided that the coefficient of variation was approximately constant for the data considered. Furthermore, it was established that the errors in the independent variables (enzyme and substrate concentrations) were small in comparison with the error in the velocity determinations. For weighting in regression analysis the inverted value of the local variance in each experimental point should be used. It was found that the
Weighted composition operators and locally convex algebras
Institute of Scientific and Technical Information of China (English)
Edoardo Vesentini
2005-01-01
The Gleason-Kahane-Zelazko theorem characterizes the continuous homomorphism of an associative, locally multiplicatively convex, sequentially complete algebra A into the field C among all linear forms on A. This characterization will be applied along two different directions. In the case in which A is a commutative Banach algebra, the theorem yields the representation of some classes of continuous linear maps A: A → A as weighted composition operators, or as composition operators when A is a continuous algebra endomorphism. The theorem will then be applied to explore the behaviour of continuous linear forms on quasi-regular elements, when A is either the algebra of all Hilbert-Schmidt operators or a Hilbert algebra.
Eiserhardt, Wolf L; Bjorholm, Stine; Svenning, Jens-Christian; Rangel, Thiago F; Balslev, Henrik
2011-01-01
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.
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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.
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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.
Non-crossing weighted kernel quantile regression with right censored data.
Bang, Sungwan; Eo, Soo-Heang; Cho, Yong Mee; Jhun, Myoungshic; Cho, HyungJun
2016-01-01
Regarding survival data analysis in regression modeling, multiple conditional quantiles are useful summary statistics to assess covariate effects on survival times. In this study, we consider an estimation problem of multiple nonlinear quantile functions with right censored survival data. To account for censoring in estimating a nonlinear quantile function, weighted kernel quantile regression (WKQR) has been developed by using the kernel trick and inverse-censoring-probability weights. However, the individually estimated quantile functions based on the WKQR often cross each other and consequently violate the basic properties of quantiles. To avoid this problem of quantile crossing, we propose the non-crossing weighted kernel quantile regression (NWKQR), which estimates multiple nonlinear conditional quantile functions simultaneously by enforcing the non-crossing constraints on kernel coefficients. The numerical results are presented to demonstrate the competitive performance of the proposed NWKQR over the WKQR.
Intuitionistic Fuzzy Weighted Linear Regression Model with Fuzzy Entropy under Linear Restrictions.
Kumar, Gaurav; Bajaj, Rakesh Kumar
2014-01-01
In fuzzy set theory, it is well known that a triangular fuzzy number can be uniquely determined through its position and entropies. In the present communication, we extend this concept on triangular intuitionistic fuzzy number for its one-to-one correspondence with its position and entropies. Using the concept of fuzzy entropy the estimators of the intuitionistic fuzzy regression coefficients have been estimated in the unrestricted regression model. An intuitionistic fuzzy weighted linear regression (IFWLR) model with some restrictions in the form of prior information has been considered. Further, the estimators of regression coefficients have been obtained with the help of fuzzy entropy for the restricted/unrestricted IFWLR model by assigning some weights in the distance function.
Schaeben, Helmut; Semmler, Georg
2016-09-01
The objective of prospectivity modeling is prediction of the conditional probability of the presence T = 1 or absence T = 0 of a target T given favorable or prohibitive predictors B, or construction of a two classes 0,1 classification of T. A special case of logistic regression called weights-of-evidence (WofE) is geologists' favorite method of prospectivity modeling due to its apparent simplicity. However, the numerical simplicity is deceiving as it is implied by the severe mathematical modeling assumption of joint conditional independence of all predictors given the target. General weights of evidence are explicitly introduced which are as simple to estimate as conventional weights, i.e., by counting, but do not require conditional independence. Complementary to the regression view is the classification view on prospectivity modeling. Boosting is the construction of a strong classifier from a set of weak classifiers. From the regression point of view it is closely related to logistic regression. Boost weights-of-evidence (BoostWofE) was introduced into prospectivity modeling to counterbalance violations of the assumption of conditional independence even though relaxation of modeling assumptions with respect to weak classifiers was not the (initial) purpose of boosting. In the original publication of BoostWofE a fabricated dataset was used to "validate" this approach. Using the same fabricated dataset it is shown that BoostWofE cannot generally compensate lacking conditional independence whatever the consecutively processing order of predictors. Thus the alleged features of BoostWofE are disproved by way of counterexamples, while theoretical findings are confirmed that logistic regression including interaction terms can exactly compensate violations of joint conditional independence if the predictors are indicators.
Waller, Niels; Jones, Jeff
2011-01-01
We describe methods for assessing all possible criteria (i.e., dependent variables) and subsets of criteria for regression models with a fixed set of predictors, x (where x is an n x 1 vector of independent variables). Our methods build upon the geometry of regression coefficients (hereafter called regression weights) in n-dimensional space. For a…
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.
Enhancement of partial robust M-regression (PRM) performance using Bisquare weight function
Mohamad, Mazni; Ramli, Norazan Mohamed; Ghani@Mamat, Nor Azura Md; Ahmad, Sanizah
2014-09-01
Partial Least Squares (PLS) regression is a popular regression technique for handling multicollinearity in low and high dimensional data which fits a linear relationship between sets of explanatory and response variables. Several robust PLS methods are proposed to accommodate the classical PLS algorithms which are easily affected with the presence of outliers. The recent one was called partial robust M-regression (PRM). Unfortunately, the use of monotonous weighting function in the PRM algorithm fails to assign appropriate and proper weights to large outliers according to their severity. Thus, in this paper, a modified partial robust M-regression is introduced to enhance the performance of the original PRM. A re-descending weight function, known as Bisquare weight function is recommended to replace the fair function in the PRM. A simulation study is done to assess the performance of the modified PRM and its efficiency is also tested in both contaminated and uncontaminated simulated data under various percentages of outliers, sample sizes and number of predictors.
Weighted linear regression using D2H and D2 as the independent variables
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...
Product Design Time Forecasting by Kernel-Based Regression with Gaussian Distribution Weights
Directory of Open Access Journals (Sweden)
Zhi-Gen Shang
2016-06-01
Full Text Available There exist problems of small samples and heteroscedastic noise in design time forecasts. To solve them, a kernel-based regression with Gaussian distribution weights (GDW-KR is proposed here. GDW-KR maintains a Gaussian distribution over weight vectors for the regression. It is applied to seek the least informative distribution from those that keep the target value within the confidence interval of the forecast value. GDW-KR inherits the benefits of Gaussian margin machines. By assuming a Gaussian distribution over weight vectors, it could simultaneously offer a point forecast and its confidence interval, thus providing more information about product design time. Our experiments with real examples verify the effectiveness and flexibility of GDW-KR.
Regression coefficient-based scoring system should be used to assign weights to the risk index.
Mehta, Hemalkumar B; Mehta, Vinay; Girman, Cynthia J; Adhikari, Deepak; Johnson, Michael L
2016-11-01
Some previously developed risk scores contained a mathematical error in their construction: risk ratios were added to derive weights to construct a summary risk score. This study demonstrates the mathematical error and derived different versions of the Charlson comorbidity score (CCS) using regression coefficient-based and risk ratio-based scoring systems to further demonstrate the effects of incorrect weighting on performance in predicting mortality. This retrospective cohort study included elderly people from the Clinical Practice Research Datalink. Cox proportional hazards regression models were constructed for time to 1-year mortality. Weights were assigned to 17 comorbidities using regression coefficient-based and risk ratio-based scoring systems. Different versions of CCS were compared using Akaike information criteria (AIC), McFadden's adjusted R(2), and net reclassification improvement (NRI). Regression coefficient-based models (Beta, Beta10/integer, Beta/Schneeweiss, Beta/Sullivan) had lower AIC and higher R(2) compared to risk ratio-based models (HR/Charlson, HR/Johnson). Regression coefficient-based CCS reclassified more number of people into the correct strata (NRI range, 9.02-10.04) compared to risk ratio-based CCS (NRI range, 8.14-8.22). Previously developed risk scores contained an error in their construction adding ratios instead of multiplying them. Furthermore, as demonstrated here, adding ratios fail to even work adequately from a practical standpoint. CCS derived using regression coefficients performed slightly better than in fitting the data compared to risk ratio-based scoring systems. Researchers should use a regression coefficient-based scoring system to develop a risk index, which is theoretically correct. Copyright Â© 2016 Elsevier Inc. All rights reserved.
Smith, Lauren H; Kuiken, Todd A; Hargrove, Levi J
2015-08-01
Regression-based prosthesis control using surface electromyography (EMG) has demonstrated real-time simultaneous control of multiple degrees of freedom (DOFs) in transradial amputees. However, these systems have been limited to control of wrist DOFs. Use of intramuscular EMG has shown promise for both wrist and hand control in able-bodied subjects, but to date has not been evaluated in amputee subjects. The objective of this study was to evaluate two regression-based simultaneous control methods using intramuscular EMG in transradial amputees and compare their performance to able-bodied subjects. Two transradial amputees and sixteen able-bodied subjects used fine wire EMG recorded from six forearm muscles to control three wrist/hand DOFs: wrist rotation, wrist flexion/extension, and hand open/close. Both linear regression and probability-weighted regression systems were evaluated in a virtual Fitts' Law test. Though both amputee subjects initially produced worse performance metrics than the able-bodied subjects, the amputee subject who completed multiple experimental blocks of the Fitts' law task demonstrated substantial learning. This subject's performance was within the range of able-bodied subjects by the end of the experiment. Both amputee subjects also showed improved performance when using probability-weighted regression for targets requiring use of only one DOF, and mirrored statistically significant differences observed with able-bodied subjects. These results indicate that amputee subjects may require more learning to achieve similar performance metrics as able-bodied subjects. These results also demonstrate that comparative findings between linear and probability-weighted regression with able-bodied subjects reflect performance differences when used by the amputee population.
Radial basis function networks with linear interval regression weights for symbolic interval data.
Su, Shun-Feng; Chuang, Chen-Chia; Tao, C W; Jeng, Jin-Tsong; Hsiao, Chih-Ching
2012-02-01
This paper introduces a new structure of radial basis function networks (RBFNs) that can successfully model symbolic interval-valued data. In the proposed structure, to handle symbolic interval data, the Gaussian functions required in the RBFNs are modified to consider interval distance measure, and the synaptic weights of the RBFNs are replaced by linear interval regression weights. In the linear interval regression weights, the lower and upper bounds of the interval-valued data as well as the center and range of the interval-valued data are considered. In addition, in the proposed approach, two stages of learning mechanisms are proposed. In stage 1, an initial structure (i.e., the number of hidden nodes and the adjustable parameters of radial basis functions) of the proposed structure is obtained by the interval competitive agglomeration clustering algorithm. In stage 2, a gradient-descent kind of learning algorithm is applied to fine-tune the parameters of the radial basis function and the coefficients of the linear interval regression weights. Various experiments are conducted, and the average behavior of the root mean square error and the square of the correlation coefficient in the framework of a Monte Carlo experiment are considered as the performance index. The results clearly show the effectiveness of the proposed structure.
Institute of Scientific and Technical Information of China (English)
Ge-mai Chen; Jin-hong You
2005-01-01
Consider a repeated measurement partially linear regression model with an unknown vector pasemiparametric generalized least squares estimator (SGLSE) ofβ, we propose an iterative weighted semiparametric least squares estimator (IWSLSE) and show that it improves upon the SGLSE in terms of asymptotic covariance matrix. An adaptive procedure is given to determine the number of iterations. We also show that when the number of replicates is less than or equal to two, the IWSLSE can not improve upon the SGLSE.These results are generalizations of those in [2] to the case of semiparametric regressions.
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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.
[Interpolation of daily mean temperature by using geographically weighted regression-Kriging].
Zhang, Guo-feng; Yang, Li-rong; Qu, Ming-kai; Chen, Hui-lin
2015-05-01
Air temperature is the input variable of numerous models in agriculture, hydrology, climate, and ecology. Currently, in study areas where the terrain is complex, methods taking into account correlation between temperature and environment variables and autocorrelation of regression residual (e.g., regression Kriging, RK) are mainly adopted to interpolate the temperature. However, such methods are based on the global ordinary least squares (OLS) regression technique, without taking into account the spatial nonstationary relationship of environment variables. Geographically weighted regression-Kriging (GWRK) is a kind of method that takes into account spatial nonstationarity relationship of environment variables and spatial autocorrelation of regression residuals of environment variables. In this study, according to the results of correlation and stepwise regression analysis, RK1 (covariates only included altitude), GWRK1 (covariates only included altitude), RK2 (covariates included latitude, altitude and closest distance to the seaside) and GWRK2 (co-variates included altitude and closest distance to the seaside) were compared to predict the spatial distribution of mean daily air temperature on Hainan Island on December 18, 2013. The prediction accuracy was assessed using the maximum positive error, maximum negative error, mean absolute error and root mean squared error based on the 80 validation sites. The results showed that GWRK1's four assessment indices were all closest to 0. The fact that RK2 and GWRK2 were worse than RK1 and GWRK1 implied that correlation among covariates reduced model performance.
A New Global Regression Analysis Method for the Prediction of Wind Tunnel Model Weight Corrections
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.
Kamil Faisal; Ahmed Shaker
2017-01-01
Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and socio-economic parameters. This paper elucidates the use of the Geographic Information System (GIS), Principal Component Analysis (PCA) and Geographically-Weighted Regression (GWR) techniques to ...
A Machine Learning Tool for Weighted Regressions in Time, Discharge, and Season
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Alexander Maestre
2014-01-01
Full Text Available A new machine learning tool has been developed to classify water stations with similar water quality trends. The tool is based on the statistical method, Weighted Regressions in Time, Discharge, and Season (WRTDS, developed by the United States Geological Survey (USGS to estimate daily concentrations of water constituents in rivers and streams based on continuous daily discharge data and discrete water quality samples collected at the same or nearby locations. WRTDS is based on parametric survival regressions using a jack-knife cross validation procedure that generates unbiased estimates of the prediction errors. One of the disadvantages of WRTDS is that it needs a large number of samples (n > 200 collected during at least two decades. In this article, the tool is used to evaluate the use of Boosted Regression Trees (BRT as an alternative to the parametric survival regressions for water quality stations with a small number of samples. We describe the development of the machine learning tool as well as an evaluation comparison of the two methods, WRTDS and BRT. The purpose of the tool is to evaluate the reduction in variability of the estimates by clustering data from nearby stations with similar concentration and discharge characteristics. The results indicate that, using clustering, the predicted concentrations using BRT are in general higher than the observed concentrations. In addition, it appears that BRT generates higher sum of square residuals than the parametric survival regressions.
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Rokhana Dwi Bekti
2014-01-01
Full Text Available Geographically Weighted Regression (GWR is a technique that brings the framework of a simple regression model into a weighted regression model. Each parameter in this model is calculated at each point geographical location. The significantly parameter can be used for mapping. In this research GWR model use for mapping Information and Communication Technology (ICT indicators which influence on illiteracy. This problem was solved by estimation GWR model. The process was developing optimum bandwidth, weighted by kernel bisquare and parameter estimation. Mapping of ICT indicators was done by P-value. This research use data 29 regencies and 9 cities in East Java Province, Indonesia. GWR model compute the variables that significantly affect on illiteracy (α = 5% in some locations, such as percent households members with a mobile phone (x_{2}, percent of household members who have computer (x_{3} and the percent of households who access the internet at school in the last month (x_{4}. Ownership of mobile phone was significant (α = 5% at 20 locations. Ownership of computer and internet access were significant at 3 locations. Coefficient determination at all locations has R^{2} between 73.05-92.75%. The factors which affecting illiteracy in each location was very diverse. Mapping by P-value or critical area shows that ownership of mobile phone significantly affected at southern part of East Java. Then, the ownership of computer and internet access were significantly affected on illiteracy at northern area. All the coefficient regression in these locations was negative. It performs that if the number of mobile phone ownership, computer ownership and internet access were high then illiteracy will be decrease.
Zhang, Hong-guang; Lu, Jian-gang
2016-02-01
Abstract To overcome the problems of significant difference among samples and nonlinearity between the property and spectra of samples in spectral quantitative analysis, a local regression algorithm is proposed in this paper. In this algorithm, net signal analysis method(NAS) was firstly used to obtain the net analyte signal of the calibration samples and unknown samples, then the Euclidean distance between net analyte signal of the sample and net analyte signal of calibration samples was calculated and utilized as similarity index. According to the defined similarity index, the local calibration sets were individually selected for each unknown sample. Finally, a local PLS regression model was built on each local calibration sets for each unknown sample. The proposed method was applied to a set of near infrared spectra of meat samples. The results demonstrate that the prediction precision and model complexity of the proposed method are superior to global PLS regression method and conventional local regression algorithm based on spectral Euclidean distance.
The refinement of partial robust M-regression model using winsorized mean and Hampel weight function
Mohamad, Mazni; Mamat, Nor Azura Md Ghani @; Ramli, Norazan Mohamed; Ahmad, Sanizah
2015-02-01
Partial Robust M-Regression (PRM) is a robust Partial Least Squares (PLS) method using M-estimator, with multivariate L1 median and a monotonous weight function, known as Fair function in its algorithm. In many studies, the use of re-descending weight functions were much preferred to monotonous weight function due to the fact that the latter often failed to assign proper weights to outliers according to their severity. With the intention of improving the performance of PRM, this study suggested slight modifications to PRM by using winsorized mean and Hampel function, which comes from the family of re-descending weight functions. The proposed method was applied to a real high dimensional dataset which then modified to contain residual outliers as well as bad leverage points. The performance of PLS, PRM and modified PRM was assessed by means of their standard error of prediction (SEP) values. Compared to classical PLS and PRM, an improved performance was observed from the proposed method.
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Collier David
2009-09-01
Full Text Available Abstract Background Obesity and weight gain are correlated with psychological ill health. We predicted that childhood emotional problems and self-perceptions predict weight gain into adulthood. Methods Data on around 6,500 individuals was taken from the 1970 Birth Cohort Study. This sample was a representative sample of individuals born in the UK in one week in 1970. Body mass index was measured by a trained nurse at the age of 10 years, and self-reported at age 30 years. Childhood emotional problems were indexed using the Rutter B scale and self-report. Self-esteem was measured using the LAWSEQ questionnaire, whilst the CARALOC scale was used to measure locus of control. Results Controlling for childhood body mass index, parental body mass index, and social class, childhood emotional problems as measured by the Rutter scale predicted weight gain in women only (least squares regression N = 3,359; coefficient 0.004; P = 0.032. Using the same methods, childhood self-esteem predicted weight gain in both men and women (N = 6,526; coefficient 0.023; P N = 6,522; coefficient 0.022; P Conclusion Emotional problems, low self-esteem and an external locus of control in childhood predict weight gain into adulthood. This has important clinical implications as it highlights a direction for early intervention strategies that may contribute to efforts to combat the current obesity epidemic.
Ternouth, Andrew; Collier, David; Maughan, Barbara
2009-09-11
Obesity and weight gain are correlated with psychological ill health. We predicted that childhood emotional problems and self-perceptions predict weight gain into adulthood. Data on around 6,500 individuals was taken from the 1970 Birth Cohort Study. This sample was a representative sample of individuals born in the UK in one week in 1970. Body mass index was measured by a trained nurse at the age of 10 years, and self-reported at age 30 years. Childhood emotional problems were indexed using the Rutter B scale and self-report. Self-esteem was measured using the LAWSEQ questionnaire, whilst the CARALOC scale was used to measure locus of control. Controlling for childhood body mass index, parental body mass index, and social class, childhood emotional problems as measured by the Rutter scale predicted weight gain in women only (least squares regression N = 3,359; coefficient 0.004; P = 0.032). Using the same methods, childhood self-esteem predicted weight gain in both men and women (N = 6,526; coefficient 0.023; P self-esteem and an external locus of control in childhood predict weight gain into adulthood. This has important clinical implications as it highlights a direction for early intervention strategies that may contribute to efforts to combat the current obesity epidemic.
Depth-weighted robust multivariate regression with application to sparse data
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.
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
Relation between weight size and degree of over-fitting in neural network regression.
Hagiwara, Katsuyuki; Fukumizu, Kenji
2008-01-01
This paper investigates the relation between over-fitting and weight size in neural network regression. The over-fitting of a network to Gaussian noise is discussed. Using re-parametrization, a network function is represented as a bounded function g multiplied by a coefficient c. This is considered to bound the squared sum of the outputs of g at given inputs away from a positive constant delta(n), which restricts the weight size of a network and enables the probabilistic upper bound of the degree of over-fitting to be derived. This reveals that the order of the probabilistic upper bound can change depending on delta(n). By applying the bound to analyze the over-fitting behavior of one Gaussian unit, it is shown that the probability of obtaining an extremely small value for the width parameter in training is close to one when the sample size is large.
Focused information criterion and model averaging based on weighted composite quantile regression
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..
Sheehan, Kenneth R.; Strager, Michael P.; Welsh, Stuart
2013-01-01
Stream habitat assessments are commonplace in fish management, and often involve nonspatial analysis methods for quantifying or predicting habitat, such as ordinary least squares regression (OLS). Spatial relationships, however, often exist among stream habitat variables. For example, water depth, water velocity, and benthic substrate sizes within streams are often spatially correlated and may exhibit spatial nonstationarity or inconsistency in geographic space. Thus, analysis methods should address spatial relationships within habitat datasets. In this study, OLS and a recently developed method, geographically weighted regression (GWR), were used to model benthic substrate from water depth and water velocity data at two stream sites within the Greater Yellowstone Ecosystem. For data collection, each site was represented by a grid of 0.1 m2 cells, where actual values of water depth, water velocity, and benthic substrate class were measured for each cell. Accuracies of regressed substrate class data by OLS and GWR methods were calculated by comparing maps, parameter estimates, and determination coefficient r 2. For analysis of data from both sites, Akaike’s Information Criterion corrected for sample size indicated the best approximating model for the data resulted from GWR and not from OLS. Adjusted r 2 values also supported GWR as a better approach than OLS for prediction of substrate. This study supports GWR (a spatial analysis approach) over nonspatial OLS methods for prediction of habitat for stream habitat assessments.
Zhao, Na; Yue, Tianxiang; Zhou, Xun; Zhao, Mingwei; Liu, Yu; Du, Zhengping; Zhang, Lili
2017-07-01
Downscaling precipitation is required in local scale climate impact studies. In this paper, a statistical downscaling scheme was presented with a combination of geographically weighted regression (GWR) model and a recently developed method, high accuracy surface modeling method (HASM). This proposed method was compared with another downscaling method using the Coupled Model Intercomparison Project Phase 5 (CMIP5) database and ground-based data from 732 stations across China for the period 1976-2005. The residual which was produced by GWR was modified by comparing different interpolators including HASM, Kriging, inverse distance weighted method (IDW), and Spline. The spatial downscaling from 1° to 1-km grids for period 1976-2005 and future scenarios was achieved by using the proposed downscaling method. The prediction accuracy was assessed at two separate validation sites throughout China and Jiangxi Province on both annual and seasonal scales, with the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE). The results indicate that the developed model in this study outperforms the method that builds transfer function using the gauge values. There is a large improvement in the results when using a residual correction with meteorological station observations. In comparison with other three classical interpolators, HASM shows better performance in modifying the residual produced by local regression method. The success of the developed technique lies in the effective use of the datasets and the modification process of the residual by using HASM. The results from the future climate scenarios show that precipitation exhibits overall increasing trend from T1 (2011-2040) to T2 (2041-2070) and T2 to T3 (2071-2100) in RCP2.6, RCP4.5, and RCP8.5 emission scenarios. The most significant increase occurs in RCP8.5 from T2 to T3, while the lowest increase is found in RCP2.6 from T2 to T3, increased by 47.11 and 2.12 mm, respectively.
Zhao, Na; Yue, Tianxiang; Zhou, Xun; Zhao, Mingwei; Liu, Yu; Du, Zhengping; Zhang, Lili
2016-03-01
Downscaling precipitation is required in local scale climate impact studies. In this paper, a statistical downscaling scheme was presented with a combination of geographically weighted regression (GWR) model and a recently developed method, high accuracy surface modeling method (HASM). This proposed method was compared with another downscaling method using the Coupled Model Intercomparison Project Phase 5 (CMIP5) database and ground-based data from 732 stations across China for the period 1976-2005. The residual which was produced by GWR was modified by comparing different interpolators including HASM, Kriging, inverse distance weighted method (IDW), and Spline. The spatial downscaling from 1° to 1-km grids for period 1976-2005 and future scenarios was achieved by using the proposed downscaling method. The prediction accuracy was assessed at two separate validation sites throughout China and Jiangxi Province on both annual and seasonal scales, with the root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE). The results indicate that the developed model in this study outperforms the method that builds transfer function using the gauge values. There is a large improvement in the results when using a residual correction with meteorological station observations. In comparison with other three classical interpolators, HASM shows better performance in modifying the residual produced by local regression method. The success of the developed technique lies in the effective use of the datasets and the modification process of the residual by using HASM. The results from the future climate scenarios show that precipitation exhibits overall increasing trend from T1 (2011-2040) to T2 (2041-2070) and T2 to T3 (2071-2100) in RCP2.6, RCP4.5, and RCP8.5 emission scenarios. The most significant increase occurs in RCP8.5 from T2 to T3, while the lowest increase is found in RCP2.6 from T2 to T3, increased by 47.11 and 2.12 mm, respectively.
Directory of Open Access Journals (Sweden)
Marcela Martínez Bascuñán
2016-11-01
Full Text Available Accessibility models in transport geography based on geographic information systems have proven to be an effective method in determining spatial inequalities associated with public health. This work aims to model the spatial accessibility from populated areas within the Concepción metropolitan area (CMA, the second largest city in Chile. The city’s public hospital network is taken into consideration with special reference to socio-regional inequalities. The use of geographically weighted regression (GWR and ordinary least squares (OLS for modelling accessibility with socioeconomic and transport variables is proposed. The explanatory variables investigated are: illiterate population, rural housing, alternative housing, homes with a motorised vehicle, public transport routes, and connectivity. Our results identify that approximately 4.1% of the population have unfavourable or very unfavourable accessibility to public hospitals, which correspond to rural areas located south of CMA. Application of a local GWR model (0.87 R2 adjusted helped to improve the settings over the use of traditional OLS methods (multiple regression (0.67 R2 adjusted and to find the spatial distribution of both coefficients of the explanatory variables, demonstrating the local significance of the model. Thus, accessibility studies have enormous potential to contribute to the development of public health and transport policies in turn to achieve equality in spatial accessibility to specialised health care.
Octavianty, Toharudin, Toni; Jaya, I. G. N. Mindra
2017-03-01
Tuberculosis (TB) is a disease caused by a bacterium, called Mycobacterium tuberculosis, which typically attacks the lungs but can also affect the kidney, spine, and brain (Centers for Disease Control and Prevention). Indonesia had the largest number of TB cases after India (Global Tuberculosis Report 2015 by WHO). The distribution of Mycobacterium tuberculosis genotypes in Indonesia showed the high genetic diversity and tended to vary by geographic regions. For instance, in Bandung city, the prevalence rate of TB morbidity is quite high. A number of TB patients belong to the counted data. To determine the factors that significantly influence the number of tuberculosis patients in each location of the observations can be used statistical analysis tool that is Geographically Weighted Poisson Regression Semiparametric (GWPRS). GWPRS is an extension of the Poisson regression and GWPR that is influenced by geographical factors, and there is also variables that influence globally and locally. Using the TB Data in Bandung city (in 2015), the results show that the global and local variables that influence the number of tuberculosis patients in every sub-district.
Drop-Weight Impact Test on U-Shape Concrete Specimens with Statistical and Regression Analyses
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Xue-Chao Zhu
2015-09-01
Full Text Available According to the principle and method of drop-weight impact test, the impact resistance of concrete was measured using self-designed U-shape specimens and a newly designed drop-weight impact test apparatus. A series of drop-weight impact tests were carried out with four different masses of drop hammers (0.875, 0.8, 0.675 and 0.5 kg. The test results show that the impact resistance results fail to follow a normal distribution. As expected, U-shaped specimens can predetermine the location of the cracks very well. It is also easy to record the cracks propagation during the test. The maximum of coefficient of variation in this study is 31.2%; it is lower than the values obtained from the American Concrete Institute (ACI impact tests in the literature. By regression analysis, the linear relationship between the first-crack and ultimate failure impact resistance is good. It can suggested that a minimum number of specimens is required to reliably measure the properties of the material based on the observed levels of variation.
Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan
2012-01-01
Multivariate local polynomial fitting is applied to the multivariate 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 non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.
DOA Estimation with Local-Peak-Weighted CSP
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Ichikawa Osamu
2010-01-01
Full Text Available This paper proposes a novel weighting algorithm for Cross-power Spectrum Phase (CSP analysis to improve the accuracy of direction of arrival (DOA estimation for beamforming in a noisy environment. Our sound source is a human speaker and the noise is broadband noise in an automobile. The harmonic structures in the human speech spectrum can be used for weighting the CSP analysis, because harmonic bins must contain more speech power than the others and thus give us more reliable information. However, most conventional methods leveraging harmonic structures require pitch estimation with voiced-unvoiced classification, which is not sufficiently accurate in noisy environments. In our new approach, the observed power spectrum is directly converted into weights for the CSP analysis by retaining only the local peaks considered to be harmonic structures. Our experiment showed the proposed approach significantly reduced the errors in localization, and it showed further improvements when used with other weighting algorithms.
Weighted Networks Model Based on Traffic Dynamics with Local Perturbation
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In the study of weighted complex networks, the interplay between traffic and topology have been paid much attention. However, the variation of topology and weight brought by new added vertices or edges should also be considered. In this paper, an evolution model of weighted networks driven by traffic dynamics with local perturbation is proposed. The model gives power-law distribution of degree, weight and strength, as confirmed by empirical measurements. By choosing appropriate parameters W and δ, the exponents of various power law distributions can be adjusted to meet real world networks. Nontrivial clustering coefficient C, degree assortativity coefficient r, and strength-degree correlation are also considered. What should be emphasized is that, with the consideration of local perturbation, one can adjust the exponent of strength-degree correlation more effectively. It makes our model more general than previous ones and may help reproducing real world networks more appropriately.
Binaural weighting of pinna cues in human sound localization
Hofman, P.M.; Opstal, A.J. van
2003-01-01
Human sound localization relies on binaural difference cues for sound-source azimuth and pinna-related spectral shape cues for sound elevation. Although the interaural timing and level difference cues are weighted to produce a percept of sound azimuth, much less is known about binaural mechanisms un
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N.W. Surya Wardhani
2014-10-01
Full Text Available Modeling of food security based on the characteristics of the area will be affected by the geographical location which means that geographical location will affect the region’s potential. Therefore, we need a method of statistical modeling that takes into account the geographical location or the location factor observations. In this case, the research variables could be global means that the location affects the response variables significantly; when some of the predictor variables are global and the other variables are local, then Geographically Weighted Ordinal Logistic Regression Semiparametric (GWOLRS could be used to analyze the data. The data used is the resilience and food insecurity data in 2011 in East Java Province. The result showed that three predictor variables that influenced by the location are the percentage of poor (%, rice production per district (tons and life expectancy (%. Those three predictor variables are local because they have significant influence in some districts/cities but had no significant effect in other districts/cities, while other two variables that are clean water and good quality road length (km are assumed global because it is not a significant factor for the whole districts/towns in East Java .
A Simple Introduction to Moving Least Squares and Local Regression Estimation
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Garimella, Rao Veerabhadra [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-06-22
In this brief note, a highly simpli ed introduction to esimating functions over a set of particles is presented. The note starts from Global Least Squares tting, going on to Moving Least Squares estimation (MLS) and nally, Local Regression Estimation (LRE).
Widyaningsih, Purnami; Retno Sari Saputro, Dewi; Nugrahani Putri, Aulia
2017-06-01
GWOLR model combines geographically weighted regression (GWR) and (ordinal logistic reression) OLR models. Its parameter estimation employs maximum likelihood estimation. Such parameter estimation, however, yields difficult-to-solve system of nonlinear equations, and therefore numerical approximation approach is required. The iterative approximation approach, in general, uses Newton-Raphson (NR) method. The NR method has a disadvantage—its Hessian matrix is always the second derivatives of each iteration so it does not always produce converging results. With regard to this matter, NR model is modified by substituting its Hessian matrix into Fisher information matrix, which is termed Fisher scoring (FS). The present research seeks to determine GWOLR model parameter estimation using Fisher scoring method and apply the estimation on data of the level of vulnerability to Dengue Hemorrhagic Fever (DHF) in Semarang. The research concludes that health facilities give the greatest contribution to the probability of the number of DHF sufferers in both villages. Based on the number of the sufferers, IR category of DHF in both villages can be determined.
Dickerman, Barbra A; Ahearn, Thomas U; Giovannucci, Edward; Stampfer, Meir J; Nguyen, Paul L; Mucci, Lorelei A; Wilson, Kathryn M
2017-09-01
Obesity is associated with an increased risk of fatal prostate cancer. We aimed to elucidate the importance and relevant timing of obesity and weight change for prostate cancer progression. We identified 5,158 men diagnosed with localized prostate cancer (clinical stage T1/T2) from 1986 to 2012 in the Health Professionals Follow-up Study. Men were followed for biochemical recurrence and lethal prostate cancer (development of distant metastasis or prostate cancer-specific mortality) until 2012. Cox regression estimated hazard ratios (HRs) for body mass index (BMI) at age 21, BMI at diagnosis, "long-term" weight change from age 21 to diagnosis and "short-term" weight change over spans of 4 and 8 years preceding diagnosis. Because weight, weight change and mortality are strongly associated with smoking, we repeated analyses among never smokers only (N = 2,559). Among all patients, neither weight change nor BMI (at age 21 or at diagnosis) was associated with lethal prostate cancer. Among never smokers, long-term weight gain was associated with an increased risk of lethal disease (HR for gaining >30 pounds vs. stable weight [±10 pounds] 1.59, 95% CI, 1.01-2.50, p-trend = 0.06). Associations between weight change, BMI and lethal prostate cancer were stronger for men with BMI ≥ 25 at age 21 compared to those with BMI obesity were not associated with an increased risk of biochemical recurrence. Our findings among never smoker men diagnosed with localized prostate cancer suggest a positive association between long-term weight gain and risk of lethal prostate cancer. Metabolic changes associated with weight gain may promote prostate cancer progression. © 2017 UICC.
López Fontán, J L; Costa, J; Ruso, J M; Prieto, G; Sarmiento, F
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.
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.)
Education-Based Gaps in eHealth: A Weighted Logistic Regression Approach.
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
Ghosh, Debarchana; Manson, Steven M.
2008-01-01
In this paper, we present a hybrid approach, robust principal component geographically weighted regression (RPCGWR), in examining urbanization as a function of both extant urban land use and the effect of social and environmental factors in the Twin Cities Metropolitan Area (TCMA) of Minnesota. We used remotely sensed data to treat urbanization via the proxy of impervious surface. We then integrated two different methods, robust principal component analysis (RPCA) and geographically weighted ...
Directory of Open Access Journals (Sweden)
Xiaobo Luo
2016-09-01
Full Text Available Urban heat island (UHI effect, the side effect of rapid urbanization, has become an obstacle to the further healthy development of the city. Understanding its relationships with impact factors is important to provide useful information for climate adaptation urban planning strategies. For this purpose, the geographically-weighted regression (GWR approach is used to explore the scale effects in a mountainous city, namely the change laws and characteristics of the relationships between land surface temperature and impact factors at different spatial resolutions (30–960 m. The impact factors include the Soil-adjusted Vegetation Index (SAVI, the Index-based Built-up Index (IBI, and the Soil Brightness Index (NDSI, which indicate the coverage of the vegetation, built-up, and bare land, respectively. For reference, the ordinary least squares (OLS model, a global regression technique, is also employed by using the same dependent variable and explanatory variables as in the GWR model. Results from the experiment exemplified by Chongqing showed that the GWR approach had a better prediction accuracy and a better ability to describe spatial non-stationarity than the OLS approach judged by the analysis of the local coefficient of determination (R2, Corrected Akaike Information Criterion (AICc, and F-test at small spatial resolution (< 240 m; however, when the spatial scale was increased to 480 m, this advantage has become relatively weak. This indicates that the GWR model becomes increasingly global, revealing the relationships with more generalized geographical patterns, and then spatial non-stationarity in the relationship will tend to be neglected with the increase of spatial resolution.
Least squares weighted twin support vector machines with local information
Institute of Scientific and Technical Information of China (English)
花小朋; 徐森; 李先锋
2015-01-01
A least squares version of the recently proposed weighted twin support vector machine with local information (WLTSVM) for binary classification is formulated. This formulation leads to an extremely simple and fast algorithm, called least squares weighted twin support vector machine with local information (LSWLTSVM), for generating binary classifiers based on two non-parallel hyperplanes. Two modified primal problems of WLTSVM are attempted to solve, instead of two dual problems usually solved. The solution of the two modified problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in WLTSVM. Moreover, two extra modifications were proposed in LSWLTSVM to improve the generalization capability. One is that a hot kernel function, not the simple-minded definition in WLTSVM, is used to define the weight matrix of adjacency graph, which ensures that the underlying similarity information between any pair of data points in the same class can be fully reflected. The other is that the weight for each point in the contrary class is considered in constructing equality constraints, which makes LSWLTSVM less sensitive to noise points than WLTSVM. Experimental results indicate that LSWLTSVM has comparable classification accuracy to that of WLTSVM but with remarkably less computational time.
WEIGHTS STAGNATION IN DYNAMIC LOCAL SEARCH FOR SAT
Directory of Open Access Journals (Sweden)
Abdelraouf Ishtaiwi
2016-05-01
Full Text Available Since 1991, tries were made to enhance the stochastic local search techniques (SLS. Some researchers turned their focus on studying the structure of the propositional satisfiability problems (SAT to better understand their complexity in order to come up with better algorithms. Other researchers focused in investigating new ways to develop heuristics that alter the search space based on some information gathered prior to or during the search process. Thus, many heuristics, enhancements and developments were introduced to improve SLS techniques performance during the last three decades. As a result a group of heuristics were introduced namely Dynamic Local Search (DLS that could outperform the systematic search techniques. Interestingly, a common characteristic of DLS heuristics is that they all depend on the use of weights during searching for satisfiable formulas. In our study we experimentally investigated the weights behaviors and movements during searching for satisfiability using DLS techniques, for simplicity, DDFW DLS heuristic is chosen. As a results of our studies we discovered that while solving hard SAT problems such as blocks world and graph coloring problems, weights stagnation occur in many areas within the search space. We conclude that weights stagnation occurrence is highly related to the level of the problem density, complexity and connectivity.
Interpreting Regression Results: beta Weights and Structure Coefficients are Both Important.
Thompson, Bruce
Various realizations have led to less frequent use of the "OVA" methods (analysis of variance--ANOVA--among others) and to more frequent use of general linear model approaches such as regression. However, too few researchers understand all the various coefficients produced in regression. This paper explains these coefficients and their…
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
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.
Local Events and Dynamics on Weighted Complex Networks
Institute of Scientific and Technical Information of China (English)
ZHAO Hui; GAO Zi-You
2006-01-01
@@ We examine the weighted networks grown and evolved by local events, such as the addition of new vertices and links and we show that depending on frequency of the events, a generalized power-law distribution of strength can emerge. Continuum theory is used to predict the scaling function as well as the exponents, which is in good agreement with the numerical simulation results. Depending on event frequency, power-law distributions of degree and weight can also be expected. Probability saturation phenomena for small strength and degree in many real world networks can be reproduced. Particularly, the non-trivial clustering coefficient, assortativity coefficient and degree-strength correlation in our model are all consistent with empirical evidences.
Small-time scale network traffic prediction based on a local support vector machine regression model
Institute of Scientific and Technical Information of China (English)
Meng Qing-Fang; Chen Yue-Hui; Peng Yu-Hua
2009-01-01
In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
Shobo, Yetty; Wong, Jen D.; Bell, Angie
2014-01-01
Regression discontinuity (RD), an "as good as randomized," research design is increasingly prominent in education research in recent years; the design gets eligible quasi-experimental designs as close as possible to experimental designs by using a stated threshold on a continuous baseline variable to assign individuals to a…
Ghosh, Debarchana; Manson, Steven M
2008-01-01
In this paper, we present a hybrid approach, robust principal component geographically weighted regression (RPCGWR), in examining urbanization as a function of both extant urban land use and the effect of social and environmental factors in the Twin Cities Metropolitan Area (TCMA) of Minnesota. We used remotely sensed data to treat urbanization via the proxy of impervious surface. We then integrated two different methods, robust principal component analysis (RPCA) and geographically weighted regression (GWR) to create an innovative approach to model urbanization. The RPCGWR results show significant spatial heterogeneity in the relationships between proportion of impervious surface and the explanatory factors in the TCMA. We link this heterogeneity to the "sprawling" nature of urban land use that has moved outward from the core Twin Cities through to their suburbs and exurbs.
Domain selection for the varying coefficient model via local polynomial regression.
Kong, Dehan; Bondell, Howard; Wu, Yichao
2015-03-01
In this article, we consider the varying coefficient model, which allows the relationship between the predictors and response to vary across the domain of interest, such as time. In applications, it is possible that certain predictors only affect the response in particular regions and not everywhere. This corresponds to identifying the domain where the varying coefficient is nonzero. Towards this goal, local polynomial smoothing and penalized regression are incorporated into one framework. Asymptotic properties of our penalized estimators are provided. Specifically, the estimators enjoy the oracle properties in the sense that they have the same bias and asymptotic variance as the local polynomial estimators as if the sparsity is known as a priori. The choice of appropriate bandwidth and computational algorithms are discussed. The proposed method is examined via simulations and a real data example.
Single Image Super-Resolution Using Global Regression Based on Multiple Local Linear Mappings.
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
DEFF Research Database (Denmark)
He, Peng; Eriksson, Frank; Scheike, Thomas H.
2016-01-01
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 cov......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...... 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...
Collier David; Ternouth Andrew; Maughan Barbara
2009-01-01
Abstract Background Obesity and weight gain are correlated with psychological ill health. We predicted that childhood emotional problems and self-perceptions predict weight gain into adulthood. Methods Data on around 6,500 individuals was taken from the 1970 Birth Cohort Study. This sample was a representative sample of individuals born in the UK in one week in 1970. Body mass index was measured by a trained nurse at the age of 10 years, and self-reported at age 30 years. Childhood emotional ...
Locally adaptive regression filter-based infrared focal plane array non-uniformity correction
Li, Jia; Qin, Hanlin; Yan, Xiang; Huang, He; Zhao, Yingjuan; Zhou, Huixin
2015-10-01
Due to the limitations of the manufacturing technology, the response rates to the same infrared radiation intensity in each infrared detector unit are not identical. As a result, the non-uniformity of infrared focal plane array, also known as fixed pattern noise (FPN), is generated. To solve this problem, correcting the non-uniformity in infrared image is a promising approach, and many non-uniformity correction (NUC) methods have been proposed. However, they have some defects such as slow convergence, ghosting and scene degradation. To overcome these defects, a novel non-uniformity correction method based on locally adaptive regression filter is proposed. First, locally adaptive regression method is used to separate the infrared image into base layer containing main scene information and the detail layer containing detailed scene with FPN. Then, the detail layer sequence is filtered by non-linear temporal filter to obtain the non-uniformity. Finally, the high quality infrared image is obtained by subtracting non-uniformity component from original image. The experimental results show that the proposed method can significantly eliminate the ghosting and the scene degradation. The results of correction are superior to the THPF-NUC and NN-NUC in the aspects of subjective visual and objective evaluation index.
Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks.
Richter, Philipp; Toledano-Ayala, Manuel
2015-09-08
Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS) signals propagate poorly. To enable wireless local area network (WLAN) location fingerprinting in larger areas while maintaining accuracy, methods to reduce the effort of radio map creation must be consolidated and automatized. Gaussian process regression has been applied to overcome this issue, also with auspicious results, but the fit of the model was never thoroughly assessed. Instead, most studies trained a readily available model, relying on the zero mean and squared exponential covariance function, without further scrutinization. This paper studies the Gaussian process regression model selection for WLAN fingerprinting in indoor and outdoor environments. We train several models for indoor/outdoor- and combined areas; we evaluate them quantitatively and compare them by means of adequate model measures, hence assessing the fit of these models directly. To illuminate the quality of the model fit, the residuals of the proposed model are investigated, as well. Comparative experiments on the positioning performance verify and conclude the model selection. In this way, we show that the standard model is not the most appropriate, discuss alternatives and present our best candidate.
Revisiting Gaussian Process Regression Modeling for Localization in Wireless Sensor Networks
Directory of Open Access Journals (Sweden)
Philipp Richter
2015-09-01
Full Text Available Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS signals propagate poorly. To enable wireless local area network (WLAN location fingerprinting in larger areas while maintaining accuracy, methods to reduce the effort of radio map creation must be consolidated and automatized. Gaussian process regression has been applied to overcome this issue, also with auspicious results, but the fit of the model was never thoroughly assessed. Instead, most studies trained a readily available model, relying on the zero mean and squared exponential covariance function, without further scrutinization. This paper studies the Gaussian process regression model selection for WLAN fingerprinting in indoor and outdoor environments. We train several models for indoor/outdoor- and combined areas; we evaluate them quantitatively and compare them by means of adequate model measures, hence assessing the fit of these models directly. To illuminate the quality of the model fit, the residuals of the proposed model are investigated, as well. Comparative experiments on the positioning performance verify and conclude the model selection. In this way, we show that the standard model is not the most appropriate, discuss alternatives and present our best candidate.
Musa, Rosliza; Ali, Zalila; Baharum, Adam; Nor, Norlida Mohd
2017-08-01
The linear regression model assumes that all random error components are identically and independently distributed with constant variance. Hence, each data point provides equally precise information about the deterministic part of the total variation. In other words, the standard deviations of the error terms are constant over all values of the predictor variables. When the assumption of constant variance is violated, the ordinary least squares estimator of regression coefficient lost its property of minimum variance in the class of linear and unbiased estimators. Weighted least squares estimation are often used to maximize the efficiency of parameter estimation. A procedure that treats all of the data equally would give less precisely measured points more influence than they should have and would give highly precise points too little influence. Optimizing the weighted fitting criterion to find the parameter estimates allows the weights to determine the contribution of each observation to the final parameter estimates. This study used polynomial model with weighted least squares estimation to investigate paddy production of different paddy lots based on paddy cultivation characteristics and environmental characteristics in the area of Kedah and Perlis. The results indicated that factors affecting paddy production are mixture fertilizer application cycle, average temperature, the squared effect of average rainfall, the squared effect of pest and disease, the interaction between acreage with amount of mixture fertilizer, the interaction between paddy variety and NPK fertilizer application cycle and the interaction between pest and disease and NPK fertilizer application cycle.
A Fast Incremental Learning for Radial Basis Function Networks Using Local Linear Regression
Ozawa, Seiichi; Okamoto, Keisuke
To avoid the catastrophic interference in incremental learning, we have proposed Resource Allocating Network with Long Term Memory (RAN-LTM). In RAN-LTM, not only new training data but also some memory items stored in long-term memory are trained either by a gradient descent algorithm or by solving a linear regression problem. In the latter approach, radial basis function (RBF) centers are not trained but selected based on output errors when connection weights are updated. The proposed incremental learning algorithm belongs to the latter approach where the errors not only for a training data but also for several retrieved memory items and pseudo training data are minimized to suppress the catastrophic interference. The novelty of the proposed algorithm is that connection weights to be learned are restricted based on RBF activation in order to improve the efficiency in learning time and memory size. We evaluate the performance of the proposed algorithm in one-dimensional and multi-dimensional function approximation problems in terms of approximation accuracy, learning time, and average memory size. The experimental results demonstrate that the proposed algorithm can learn fast and have good performance with less memory size compared to memory-based learning methods.
Ciotoli, G; Voltaggio, M; Tuccimei, P; Soligo, M; Pasculli, A; Beaubien, S E; Bigi, S
2017-01-01
In many countries, assessment programmes are carried out to identify areas where people may be exposed to high radon levels. These programmes often involve detailed mapping, followed by spatial interpolation and extrapolation of the results based on the correlation of indoor radon values with other parameters (e.g., lithology, permeability and airborne total gamma radiation) to optimise the radon hazard maps at the municipal and/or regional scale. In the present work, Geographical Weighted Regression and geostatistics are used to estimate the Geogenic Radon Potential (GRP) of the Lazio Region, assuming that the radon risk only depends on the geological and environmental characteristics of the study area. A wide geodatabase has been organised including about 8000 samples of soil-gas radon, as well as other proxy variables, such as radium and uranium content of homogeneous geological units, rock permeability, and faults and topography often associated with radon production/migration in the shallow environment. All these data have been processed in a Geographic Information System (GIS) using geospatial analysis and geostatistics to produce base thematic maps in a 1000 m × 1000 m grid format. Global Ordinary Least Squared (OLS) regression and local Geographical Weighted Regression (GWR) have been applied and compared assuming that the relationships between radon activities and the environmental variables are not spatially stationary, but vary locally according to the GRP. The spatial regression model has been elaborated considering soil-gas radon concentrations as the response variable and developing proxy variables as predictors through the use of a training dataset. Then a validation procedure was used to predict soil-gas radon values using a test dataset. Finally, the predicted values were interpolated using the kriging algorithm to obtain the GRP map of the Lazio region. The map shows some high GRP areas corresponding to the volcanic terrains (central
Montiel, Ariadna; Sendra, Irene; Escamilla-Rivera, Celia; Salzano, Vincenzo
2014-01-01
In this work we present a nonparametric approach, which works on minimal assumptions, to reconstruct the cosmic expansion of the Universe. We propose to combine a locally weighted scatterplot smoothing method and a simulation-extrapolation method. The first one (Loess) is a nonparametric approach that allows to obtain smoothed curves with no prior knowledge of the functional relationship between variables nor of the cosmological quantities. The second one (Simex) takes into account the effect of measurement errors on a variable via a simulation process. For the reconstructions we use as raw data the Union2.1 Type Ia Supernovae compilation, as well as recent Hubble parameter measurements. This work aims to illustrate the approach, which turns out to be a self-sufficient technique in the sense we do not have to choose anything by hand. We examine the details of the method, among them the amount of observational data needed to perform the locally weighted fit which will define the robustness of our reconstructio...
DEFF Research Database (Denmark)
Eiserhardt, Wolf L.; Bjorholm, Stine; Svenning, J.-C.
2011-01-01
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...
Pac-bayesian bounds for sparse regression estimation with exponential weights
Alquier, Pierre
2010-01-01
We consider the sparse regression model where the number of parameters $p$ is larger than the sample size $n$. The difficulty when considering high-dimensional problems is to propose estimators achieving a good compromise between statistical and computational performances. The BIC estimator for instance performs well from the statistical point of view \\cite{BTW07} but can only be computed for values of $p$ of at most a few tens. The Lasso estimator is solution of a convex minimization problem, hence computable for large value of $p$. However stringent conditions on the design are required to establish fast rates of convergence for this estimator. Dalalyan and Tsybakov \\cite{arnak} propose a method achieving a good compromise between the statistical and computational aspects of the problem. Their estimator can be computed for reasonably large $p$ and satisfies nice statistical properties under weak assumptions on the design. However, \\cite{arnak} proposes sparsity oracle inequalities in expectation for the emp...
Directory of Open Access Journals (Sweden)
Abobaker M. Jaber
2014-01-01
Full Text Available Empirical mode decomposition (EMD is particularly useful in analyzing nonstationary and nonlinear time series. However, only partial data within boundaries are available because of the bounded support of the underlying time series. Consequently, the application of EMD to finite time series data results in large biases at the edges by increasing the bias and creating artificial wiggles. This study introduces a new two-stage method to automatically decrease the boundary effects present in EMD. At the first stage, local polynomial quantile regression (LLQ is applied to provide an efficient description of the corrupted and noisy data. The remaining series is assumed to be hidden in the residuals. Hence, EMD is applied to the residuals at the second stage. The final estimate is the summation of the fitting estimates from LLQ and EMD. Simulation was conducted to assess the practical performance of the proposed method. Results show that the proposed method is superior to classical EMD.
Spatialization of climatic data at the Italian national level by local regressive models
Directory of Open Access Journals (Sweden)
Blasi C
2007-01-01
Full Text Available The availability of spatialised climatic data is an essential pre-requisite for the implementation of GIS-based analysis in many application fields. Among the different methodologies for the spatialization of climatic data collected in weather-stations the most used are those based on geostatistical approaches, on parametric correlative models or on neural networks. Within the “Completamento delle Conoscenze Naturalistiche di Base” project, funded by the Italian Ministry for the Environment (Department of Nature Protection a database of 403 weather-stations distributed across Italy with a time series of thirty years was collected. Data of mean monthly temperature (minimum and maximum and rainfalls were spatialized by a local linear univariate regressive method based on elevation as independent variable. A total of 36 monthly maps with a geometric resolution of 250 m was generated. The present paper introduces the adopted methodology and the accuracy results estimated by leave-one-out cross validation.
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.
DEFF Research Database (Denmark)
Shirali, Mahmoud; Nielsen, Vivi Hunnicke; Møller, Steen Henrik
2014-01-01
The aim of this study was to determine genetic background of longitudinal residual feed intake (RFI) and body weight (BW) growth in farmed mink using random regression methods considering heterogeneous residual variances. Eight BW measures for each mink was recorded every three weeks from 63 to 210...... days of age for 2139 male mink and the same number of females. Cumulative feed intake was calculated six times with three weeks interval based on daily feed consumption between weighing’s from 105 to 210 days of age. Heritability estimates for RFI increased by age from 0.18 (0.03, standard deviation...... be obtained by only considering RFI estimate and BW at pelting, however, lower genetic correlations than unity indicate that extra genetic gain can be obtained by including estimates of these traits at the growing period. This study suggests random regression methods are suitable for analysing feed efficiency...
Modeling Source Water TOC Using Hydroclimate Variables and Local Polynomial Regression.
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.
Directory of Open Access Journals (Sweden)
Jaehyun Yoo
2015-05-01
Full Text Available Machine learning has been successfully used for target localization in wireless sensor networks (WSNs due to its accurate and robust estimation against highly nonlinear and noisy sensor measurement. For efficient and adaptive learning, this paper introduces online semi-supervised support vector regression (OSS-SVR. The first advantage of the proposed algorithm is that, based on semi-supervised learning framework, it can reduce the requirement on the amount of the labeled training data, maintaining accurate estimation. Second, with an extension to online learning, the proposed OSS-SVR automatically tracks changes of the system to be learned, such as varied noise characteristics. We compare the proposed algorithm with semi-supervised manifold learning, an online Gaussian process and online semi-supervised colocalization. The algorithms are evaluated for estimating the unknown location of a mobile robot in a WSN. The experimental results show that the proposed algorithm is more accurate under the smaller amount of labeled training data and is robust to varying noise. Moreover, the suggested algorithm performs fast computation, maintaining the best localization performance in comparison with the other methods.
Local classification: Locally weighted-partial least squares-discriminant analysis (LW-PLS-DA).
Bevilacqua, Marta; Marini, Federico
2014-08-01
The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks).
Energy Technology Data Exchange (ETDEWEB)
Cosemans, G.; Kretzschmar, J. [Flemish Inst. for Technological Research (Vito), Mol (Belgium)
2004-07-01
Pollutant roses are polar diagrams that show how air pollution depends on wind direction. If an ambient air quality monitoring station is markedly influenced by a source of the pollutant measured, the pollutant rose shows a peak towards the local source. When both wind direction data and pollutant concentration are measured as (1/2)-hourly averages, the pollutant rose is mathematically well defined and the computation is simple. When the pollutant data are averages over 24 h, as is the case for heavy metals or dioxin levels or in many cases PM10-levels in ambient air, the pollutant rose is mathematically well defined, but the computational scheme is not obvious. In this paper, two practical methods to maximize the information content of pollutant roses based on 24 h pollutant concentrations are presented. These methods are applied to time series of 24 h SO{sub 2} concentrations, derived from the 1/2-hourly SO{sub 2} concentrations measured in the Antwerp harbour, industrial, urban and rural regions by the Telemetric Air Quality Monitoring Network of the Flemish Environmental Agency (VMM). The pollutant roses computed from the 1/2-hourly SO{sub 2} concentrations constitute reference or control-roses to evaluate the representativeness or truthfulness of pollutant roses obtained by the presented methods. The presented methodology is very useful in model validations that have to be based on measured daily averaged concentrations as only available real ambient levels. While the methods give good pollutant roses in general, this paper especially deals with the case of pollutant roses with 'false' peaks. (orig.)
Gholizadeh, H.; Robeson, S. M.
2015-12-01
Empirical models have been widely used to estimate global chlorophyll content from remotely sensed data. Here, we focus on the standard NASA empirical models that use blue-green band ratios. These band ratio ocean color (OC) algorithms are in the form of fourth-order polynomials and the parameters of these polynomials (i.e. coefficients) are estimated from the NASA bio-Optical Marine Algorithm Data set (NOMAD). Most of the points in this data set have been sampled from tropical and temperate regions. However, polynomial coefficients obtained from this data set are used to estimate chlorophyll content in all ocean regions with different properties such as sea-surface temperature, salinity, and downwelling/upwelling patterns. Further, the polynomial terms in these models are highly correlated. In sum, the limitations of these empirical models are as follows: 1) the independent variables within the empirical models, in their current form, are correlated (multicollinear), and 2) current algorithms are global approaches and are based on the spatial stationarity assumption, so they are independent of location. Multicollinearity problem is resolved by using partial least squares (PLS). PLS, which transforms the data into a set of independent components, can be considered as a combined form of principal component regression (PCR) and multiple regression. Geographically weighted regression (GWR) is also used to investigate the validity of spatial stationarity assumption. GWR solves a regression model over each sample point by using the observations within its neighbourhood. PLS results show that the empirical method underestimates chlorophyll content in high latitudes, including the Southern Ocean region, when compared to PLS (see Figure 1). Cluster analysis of GWR coefficients also shows that the spatial stationarity assumption in empirical models is not likely a valid assumption.
Kauhl, Boris; Pieper, Jonas; Schweikart, Jürgen; Keste, Andrea; Moskwyn, Marita
2017-02-16
Understanding which population groups in which locations are at higher risk for type 2 diabetes mellitus (T2DM) allows efficient and cost-effective interventions targeting these risk-populations in great need in specific locations. The goal of this study was to analyze the spatial distribution of T2DM and to identify the location-specific, population-based risk factors using global and local spatial regression models. To display the spatial heterogeneity of T2DM, bivariate kernel density estimation was applied. An ordinary least squares regression model (OLS) was applied to identify population-based risk factors of T2DM. A geographically weighted regression model (GWR) was then constructed to analyze the spatially varying association between the identified risk factors and T2DM. T2DM is especially concentrated in the east and outskirts of Berlin. The OLS model identified proportions of persons aged 80 and older, persons without migration background, long-term unemployment, households with children and a negative association with single-parenting households as socio-demographic risk groups. The results of the GWR model point out important local variations of the strength of association between the identified risk factors and T2DM. The risk factors for T2DM depend largely on the socio-demographic composition of the neighborhoods in Berlin and highlight that a one-size-fits-all approach is not appropriate for the prevention of T2DM. Future prevention strategies should be tailored to target location-specific risk-groups. © Georg Thieme Verlag KG Stuttgart · New York.
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
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.
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
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Liang Wu
2016-11-01
Full Text Available Severe fever with thrombocytopenia syndrome (SFTS is caused by severe fever with thrombocytopenia syndrome virus (SFTSV, which has had a serious impact on public health in parts of Asia. There is no specific antiviral drug or vaccine for SFTSV and, therefore, it is important to determine the factors that influence the occurrence of SFTSV infections. This study aimed to explore the spatial associations between SFTSV infections and several potential determinants, and to predict the high-risk areas in mainland China. The analysis was carried out at the level of provinces in mainland China. The potential explanatory variables that were investigated consisted of meteorological factors (average temperature, average monthly precipitation and average relative humidity, the average proportion of rural population and the average proportion of primary industries over three years (2010–2012. We constructed a geographically weighted logistic regression (GWLR model in order to explore the associations between the selected variables and confirmed cases of SFTSV. The study showed that: (1 meteorological factors have a strong influence on the SFTSV cover; (2 a GWLR model is suitable for exploring SFTSV cover in mainland China; (3 our findings can be used for predicting high-risk areas and highlighting when meteorological factors pose a risk in order to aid in the implementation of public health strategies.
Spectral covolatility estimation from noisy observations using local weights
Bibinger, Markus
2011-01-01
We propose localized spectral estimators for the quadratic covariation and the spot covolatility of diffusion processes which are observed discretely with additive observation noise. The eligibility of this approach to lead to an appropriate estimation for time-varying volatilities stems from an asymptotic equivalence of the underlying statistical model to a white noise model with correlation and volatility processes being constant over small intervals. The asymptotic equivalence of the continuous-time and the discrete-time experiments are proved by a construction with linear interpolation in one direction and local means for the other. The new estimator outperforms earlier nonparametric approaches in the considered model. We investigate its finite sample size characteristics in simulations and draw a comparison between the various proposed methods.
Riley, D G; Coleman, S W; Chase, C C; Olson, T A; Hammond, A C
2007-01-01
The objective of this research was to assess the genetic control of BW, hip height, and the ratio of BW to hip height (n = 5,055) in Brahman cattle through 170 d on feed using covariance function-random regression models. A progeny test of Brahman sires (n = 27) generated records of Brahman steers and heifers (n = 724) over 7 yr. Each year after weaning, calves were assigned to feedlot pens, where they were fed a high-concentrate grain diet. Body weights and hip heights were recorded every 28 d until cattle reached a targeted fatness level. All calves had records through 170 d on feed; subsequent records were excluded. Models included contemporary group (sex-pen-year combinations, n = 63) and age at the beginning of the feeding period as a covariate. The residual error structure was modeled as a random effect, with 2 levels corresponding to two 85-d periods on feed. Information criterion values indicated that linear, random regression coefficients on Legendre polynomials of days on feed were most appropriate to model additive genetic effects for all 3 traits. Cubic (hip height and BW:hip height ratio) or quartic (BW) polynomials best modeled permanent environmental effects. Estimates of heritability across the 170-d feeding period ranged from 0.31 to 0.53 for BW, from 0.37 to 0.53 for hip height, and from 0.23 to 0.6 for BW:hip height ratio. Estimates of the permanent environmental proportion of phenotypic variance ranged from 0.44 to 0.58 for BW, 0.07 to 0.26 for hip height, and 0.30 to 0.48 for BW:hip height ratio. Within-trait estimates of genetic correlation on pairs of days on feed (at 28-d intervals) indicated lower associations of BW:hip height ratio EBV early and late in the feeding period but large positive associations for BW or hip height EBV throughout. Estimates of genetic correlations among the 3 traits indicated almost no association of BW:hip height ratio and hip height EBV. The ratio of BW to hip height in cattle has previously been used as an
Power-law weighted networks from local attachments
Moriano, P
2011-01-01
Large networks arise by the gradual addition of nodes attaching to an existing and evolving network component. There are a wide class of attachment strategies which lead to distinct structural features in growing networks. This paper introduces a mechanism for constructing, through a process of distributed decision-making, substrates for the study of collective dynamics on power-law weighted networks with both a desired scaling exponent and a fixed clustering coefficient. The analytical results show that the connectivity distribution converges to the scaling behavior often found in social and engineering systems. To illustrate the approach of the proposed framework we generate network substrates that resemble the empirical citation distributions of (i) publications indexed by the Institute for Scientific Information from 1981 to 1997; (ii) patents granted by the U.S. Patent and Trademark Office from 1975 to 1999; and (iii) opinions written by the Supreme Court and the cases they cite from 1754 to 2002.
Directory of Open Access Journals (Sweden)
P. Arockia Jansi Rani
2010-08-01
Full Text Available Image compression is very important in reducing the costs of data storage and transmission in relatively slow channels. In this paper, a still image compression scheme driven by Self-Organizing Map with polynomial regression modeling and entropy coding, employed within the wavelet framework is presented. The image compressibility and interpretability are improved by incorporating noise reduction into the compression scheme. The implementation begins with the classical wavelet decomposition, quantization followed by Huffman encoder. The codebook for the quantization process is designed using an unsupervised learning algorithm and further modified using polynomial regression to control the amount of noise reduction. Simulation results show that the proposed method reduces bit rate significantly and provides better perceptual quality than earlier methods.
P. Arockia Jansi Rani; V. Sadasivam
2010-01-01
Image compression is very important in reducing the costs of data storage and transmission in relatively slow channels. In this paper, a still image compression scheme driven by Self-Organizing Map with polynomial regression modeling and entropy coding, employed within the wavelet framework is presented. The image compressibility and interpretability are improved by incorporating noise reduction into the compression scheme. The implementation begins with the classical wavelet decomposition, q...
Generating weighted community networks based on local events
Institute of Scientific and Technical Information of China (English)
Xu Qi-Xin; Xu Xin-Jian
2009-01-01
realistic networks have community structures, namely, a network consists of groups of nodes within which links are dense but among which links are sparse. This paper proposes a growing network model based on local processes, the addition of new nodes intra-community and new links intra- or inter-community. Also, it utilizes the preferential attachment for building connections determined by nodes' strengths, which evolves dynamically during the growth of the system. The resulting network reflects the intrinsic community structure with generalized power-law distributions of nodes' degrees and strengths.
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...
Automated linear regression tools improve RSSI WSN localization in multipath indoor environment
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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.
Tsai, Pui-Jen; Yeh, Hsi-Chyi
2013-04-29
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. 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. 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 with scrub typhus incidence in
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Francesca M. Sarti
2015-07-01
Full Text Available The Appenninica breed is an Italian meat sheep; the rams are approved according to a phenotypic index that is based on an average daily gain at performance test. The 8546 live weights of 1930 Appenninica male lambs tested in the performance station of the ASSONAPA (National Sheep Breeders Association, Italy from 1986 to 2010 showed a great variability in age at weighing and in number of records by year. The goal of the study is to verify the feasibility of the estimation of a genetic index for weight in the Appenninica sheep by a mixed model, and to explore the use of random regression to avoid the corrections for weighing at different ages. The heritability and repeatability (mean±SE of the average live weight were 0.27±0.04 and 0.54±0.08 respectively; the heritabilities of weights recorded at different weighing days ranged from 0.27 to 0.58, while the heritabilities of weights at different ages showed a narrower variability (0.29÷0.41. The estimates of live weight heritability by random regressions ranged between 0.34 at 123 d of age and 0.52 at 411 d. The results proved that the random regression model is the most adequate to analyse the data of Appenninica breed.
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.
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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.
Niemeijer, Meindert; Dumitrescu, Alina V.; van Ginneken, Bram; Abrámoff, Michael D.
2011-03-01
Parameters extracted from the vasculature on the retina are correlated with various conditions such as diabetic retinopathy and cardiovascular diseases such as stroke. Segmentation of the vasculature on the retina has been a topic that has received much attention in the literature over the past decade. Analysis of the segmentation result, however, has only received limited attention with most works describing methods to accurately measure the width of the vessels. Analyzing the connectedness of the vascular network is an important step towards the characterization of the complete vascular tree. The retinal vascular tree, from an image interpretation point of view, originates at the optic disc and spreads out over the retina. The tree bifurcates and the vessels also cross each other. The points where this happens form the key to determining the connectedness of the complete tree. We present a supervised method to detect the bifurcations and crossing points of the vasculature of the retina. The method uses features extracted from the vasculature as well as the image in a location regression approach to find those locations of the segmented vascular tree where the bifurcation or crossing occurs (from here, POI, points of interest). We evaluate the method on the publicly available DRIVE database in which an ophthalmologist has marked the POI.
Jaber, Abobaker M; Ismail, Mohd Tahir; Altaher, Alsaidi M
2014-01-01
This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD) with nonparametric methods of local linear quantile (LLQ). We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.
Directory of Open Access Journals (Sweden)
Abobaker M. Jaber
2014-01-01
Full Text Available This paper mainly forecasts the daily closing price of stock markets. We propose a two-stage technique that combines the empirical mode decomposition (EMD with nonparametric methods of local linear quantile (LLQ. We use the proposed technique, EMD-LLQ, to forecast two stock index time series. Detailed experiments are implemented for the proposed method, in which EMD-LPQ, EMD, and Holt-Winter methods are compared. The proposed EMD-LPQ model is determined to be superior to the EMD and Holt-Winter methods in predicting the stock closing prices.
Use of large bulls to improve the body weight of local small sized buffalo
Directory of Open Access Journals (Sweden)
M. Van Sanh
2010-02-01
Full Text Available Eight Swamp buffalo bulls (4 large and 4 local small sized and 240 buffalo cows (120 selected and 120 non-selected were used to evaluate the effects of bull size and selected cows on body weight of calves. Experimental animals were allocated into 4 groups: T1- large sized bulls x selected cows (BSB+SC; T2 - large sized bulls x non-selected cows (BSB+NSC; T3 – local small sized bulls x selected cows (SSB+SC and CT – local small sized bulls x non-selected cows (SSB+NSC as a control group. Each bull was mated with 15 selected cows and 15 non-selected cows. Body weight of calves at birth, 3, 6, 12 and 24 months of ages was highest in calves of T1, then T2 and then T3 while the lowest weight was found in the CT group. Calf weight in the large sized bulls and selected cows group was higher by 10-15% than that of calves in the local small bulls and non-selected cows group at all ages. It is concluded that the use of large sized bulls for breeding increased body weight of calves. Using large bulls and selected buffalo cows was the best solution for improving the body weight of the local buffalo.
Zhang, Dongqing; Liu, Yuan; Noble, Jack H.; Dawant, Benoit M.
2016-03-01
Cochlear Implants (CIs) are electrode arrays that are surgically inserted into the cochlea. Individual contacts stimulate frequency-mapped nerve endings thus replacing the natural electro-mechanical transduction mechanism. CIs are programmed post-operatively by audiologists but this is currently done using behavioral tests without imaging information that permits relating electrode position to inner ear anatomy. We have recently developed a series of image processing steps that permit the segmentation of the inner ear anatomy and the localization of individual contacts. We have proposed a new programming strategy that uses this information and we have shown in a study with 68 participants that 78% of long term recipients preferred the programming parameters determined with this new strategy. A limiting factor to the large scale evaluation and deployment of our technique is the amount of user interaction still required in some of the steps used in our sequence of image processing algorithms. One such step is the rough registration of an atlas to target volumes prior to the use of automated intensity-based algorithms when the target volumes have very different fields of view and orientations. In this paper we propose a solution to this problem. It relies on a random forest-based approach to automatically localize a series of landmarks. Our results obtained from 83 images with 132 registration tasks show that automatic initialization of an intensity-based algorithm proves to be a reliable technique to replace the manual step.
Liu, Jiaqi; Han, Jing; Zhang, Yi; Bai, Lianfa
2015-10-01
Locally adaptive regression kernels model can describe the edge shape of images accurately and graphic trend of images integrally, but it did not consider images' color information while the color is an important element of an image. Therefore, we present a novel method of target recognition based on 3-D-color-space locally adaptive regression kernels model. Different from the general additional color information, this method directly calculate the local similarity features of 3-D data from the color image. The proposed method uses a few examples of an object as a query to detect generic objects with incompact, complex and changeable shapes. Our method involves three phases: First, calculating the novel color-space descriptors from the RGB color space of query image which measure the likeness of a voxel to its surroundings. Salient features which include spatial- dimensional and color -dimensional information are extracted from said descriptors, and simplifying them to construct a non-similar local structure feature set of the object class by principal components analysis (PCA). Second, we compare the salient features with analogous features from the target image. This comparison is done using a matrix generalization of the cosine similarity measure. Then the similar structures in the target image are obtained using local similarity structure statistical matching. Finally, we use the method of non-maxima suppression in the similarity image to extract the object position and mark the object in the test image. Experimental results demonstrate that our approach is effective and accurate in improving the ability to identify targets.
Image-based human age estimation by manifold learning and locally adjusted robust regression.
Guo, Guodong; Fu, Yun; Dyer, Charles R; Huang, Thomas S
2008-07-01
Estimating human age automatically via facial image analysis has lots of potential real-world applications, such as human computer interaction and multimedia communication. However, it is still a challenging problem for the existing computer vision systems to automatically and effectively estimate human ages. The aging process is determined by not only the person's gene, but also many external factors, such as health, living style, living location, and weather conditions. Males and females may also age differently. The current age estimation performance is still not good enough for practical use and more effort has to be put into this research direction. In this paper, we introduce the age manifold learning scheme for extracting face aging features and design a locally adjusted robust regressor for learning and prediction of human ages. The novel approach improves the age estimation accuracy significantly over all previous methods. The merit of the proposed approaches for image-based age estimation is shown by extensive experiments on a large internal age database and the public available FG-NET database.
Weighted Local Active Pixel Pattern (WLAPP for Face Recognition in Parallel Computation Environment
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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.
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...
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Ramzi N. Nasser
2010-01-01
Full Text Available Problem statement: Mathematically little is known of college admission criteria as in school grade point average, admission test scores or rank in class and weighting of the criteria into a composite equation. Approach: This study presented a method to obtain weights on composite admission equation. The method uses an iterative procedure to build a prediction equation for an optimal weighted admission composite score. The three-predictor variables, high school average, entrance exam scores and rank in class, were regressed on college Grade Point Average (GPA. The weights for the composite equation were determined through regression coefficients and numerical approach that correlate the composite score with college GPA. Results: A set of composite equations were determined with the weights on each criteria in a composite equation. Conclusion: This study detailed a substantiated algorithm and based on an optimal composite score, comes out with an original and unique structured composite score equation for admissions, which can be used by admission officers at colleges and universities.
Nakagawa, Hiroshi; Tajima, Takahiro; Kano, Manabu; Kim, Sanghong; Hasebe, Shinji; Suzuki, Tatsuya; Nakagami, Hiroaki
2012-04-17
The usefulness of infrared-reflection absorption spectroscopy (IR-RAS) for the rapid measurement of residual drug substances without sampling was evaluated. In order to realize the highly accurate rapid measurement, locally weighted partial least-squares (LW-PLS) with a new weighting technique was developed. LW-PLS is an adaptive method that builds a calibration model on demand by using a database whenever prediction is required. By adding more weight to samples closer to a query, LW-PLS can achieve higher prediction accuracy than PLS. In this study, a new weighting technique is proposed to further improve the prediction accuracy of LW-PLS. The root-mean-square error of prediction (RMSEP) of the IR-RAS spectra analyzed by LW-PLS with the new weighting technique was compared with that analyzed by PLS and locally weighted regression (LWR). The RMSEP of LW-PLS with the proposed weighting technique was about 36% and 14% smaller than that of PLS and LWR, respectively, when ibuprofen was a residual drug substance. Similarly, LW-PLS with the weighting technique was about 39% and 24% better than PLS and LWR in RMSEP, respectively, when magnesium stearate was a residual excipient. The combination of IR-RAS and LW-PLS with the proposed weighting technique is a very useful rapid measurement technique of the residual drug substances.
Li, Changjun; Oleari, Claudio; Melgosa, Manuel; Xu, Yang
2011-11-01
In this paper, two types of weighting tables are derived by applying the local power expansion method proposed by Oleari [Color Res. Appl. 25, 176 (2000)]. Both tables at two different levels consider the deconvolution of the spectrophotometric data for monochromator triangular transmittance. The first one, named zero-order weighting table, is similar to weighting table 5 of American Society for Testing and Materials (ASTM) used with the measured spectral reflectance factors (SRFs) corrected by the Stearns and Stearns formula. The second one, named second-order weighting table, is similar to weighting table 6 of ASTM and must be used with the undeconvoluted SRFs. It is hoped that the results of this paper will aid the International Commission on Illumination TC 1-71 on tristimulus integration in focusing on ongoing methods, testing, and recommendations.
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
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
graphs. We implement all algorithms in a publicly available tool prototype and evaluate them on several experiments. The principal conclusion is that our local algorithm is the most efficient one with an order of magnitude improvement for model checking problems with a high number of “witnesses”.......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...... boolean domain to nonnegative integers in order to cope with the weights. We present both global and local algorithms for the fixed-point computation on symbolic dependency graphs and argue for the advantages of our approach compared to the direct encoding of the model checking problem into dependency...
Marcela Martínez Bascuñán; Carolina Rojas Quezada
2016-01-01
Accessibility models in transport geography based on geographic information systems have proven to be an effective method in determining spatial inequalities associated with public health. This work aims to model the spatial accessibility from populated areas within the Concepción metropolitan area (CMA), the second largest city in Chile. The city’s public hospital network is taken into consideration with special reference to socio-regional inequalities. The use of geographically weighted reg...
Ishii, Nozomu; Shiga, Hiroki; Ikarashi, Naoto; Sato, Ken-Ichi; Hamada, Lira; Watanabe, Soichi
As a technique for calibrating electric-field probes used in standardized SAR (Specific Absorption Rate) assessment, we have studied the technique using the Friis transmission formula in the tissue-equivalent liquid. It is difficult to measure power transmission between two reference antennas in the far-field region due to large attenuation in the liquid. This means that the conventional Friis transmission formula cannot be applied to our measurement so that we developed an extension of this formula that is valid in the near-field region. In this paper, the method of weighted least squares is introduced to reduce the effect of the noise in the measurement system when the gain of the antenna operated in the liquid is determined by the curve-fitting technique. And we examine how to choose the fitting range to reduce the uncertainty of the estimated gain.
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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.
Murphy, Jessica; Moullec, Grégory; Santosa, Sylvia
2017-02-01
Enlarged adipocytes are a prime feature of adipose tissue dysfunction, and may be an appropriate target to decrease disease risk in obesity. We aimed to assess the change in adipocyte size in response to lifestyle and surgical weight loss interventions for overweight or obesity; and to explore whether certain participant and intervention characteristics influence this response. We systematically searched MEDLINE, EMBASE, CINAHL and Cochrane electronic databases to identify weight loss studies that quantified adipocyte size before and after the intervention. Using meta-regression analysis, we assessed the independent effects of weight loss, age, sex, adipocyte region, and intervention type (surgical vs. lifestyle) on adipocyte size reduction. We repeated the model as a sensitivity analysis including only the lifestyle interventions. Thirty-five studies met our eligibility criteria. In our main model, every 1.0% weight loss was associated with a 0.64% reduction in adipocyte size (p=0.003); and adipocytes from the upper body decreased 5% more in size than those in the lower body (p=0.009). These relationships were no longer significant when focusing only on lifestyle interventions. Moreover, age, sex and intervention type did not independently affect adipocyte size reduction in either model. Weight loss in obese individuals is consistently associated with a decrease in adipocyte size that is more pronounced in upper-body adipocytes. It remains to be clarified how biological differences and intervention characteristics influence this relationship, and whether it corresponds with reductions in other aspects of adipose tissue dysfunction and disease risk. Copyright © 2016 Elsevier Inc. All rights reserved.
Noise Reduction and Gap Filling of fAPAR Time Series Using an Adapted Local Regression Filter
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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
Polymorphism of Calpastatin gene and its effect on body weight of local sheeps
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C Sumantri
2008-06-01
Full Text Available The objectives of this research were to identify polymorphism of calpastatin gene and to investigate any association of calpastatin genotype on body weight of local sheeps. A total number of DNA samples were collected from 288 heads of local sheeps from 8 populations. Two local sheep samples were medium tail sheeps (MTSs of a Garut fighting type from Ciomas/Bogor (29 and a Garut meat type from Margawati (29. The remaining six local sheep population were one thin tail sheep (TTS from Jonggol (36; and five fat tail (FTSs from Indramayu (43, Madura (43, Sumbawa (26, Rote (36 and Donggala (46 respectively. Genomic DNAs of those blood of local sheeps were extracted by a standard phenol-chloroform protocol and amplified using a polymerase chain reaction (PCR technique. PCR reaction was carried out in a thermocycler (Takara PCR of Thermal Cycler MP4 and PCR products were digested with Msp 1 enzyme restriction using a Restriction Fragment Length Polymorphism (RFLP technique. The PCR-RFLP products were separated at 8% polyacrylamide gel electrophoresis (PAGE. A silver-staining method then was applied to detect fragments. Genetic variations between local sheep populations were calculated based on frequencies of genotypes and alelles. The association between genotype of calpastatin gene and body weight of local sheeps were calculated by General Linear Model method by SAS version 6.12. A length of 622 base pairs (bp of the calpastatin gene of the Indonesian local sheeps was successfully amplified by the PCR technique. An MspI restriction enzyme cut the PCR product into two different length fragments, those were 336 bp and 286 bp designated as M allele of the CAST-Msp1; whilst that unsuccessfully cut PCR product resulted one fragment 622 bp designated as N allele of the CAST-Msp1. Locus of the CAST-Msp1 gene in most local sheeps studied was polymorphic, the exception was in the FTS from Rote of which monomorphic. The highest frequency of the M allele was in
Harris, Richard J.; McNeil, Keith
1993-01-01
Presents two viewpoints about the use and interpretability of beta weights in educational research: (1) that beta weights should be interpreted as a logical index of the importance of individual predictors within the context of the entire set of predictors; and (2) that interpretation requires certain cautions and conditions. (SV)
Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization
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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.
Weighted least squares techniques for improved received signal strength based localization.
Tarrío, Paula; Bernardos, Ana M; Casar, José R
2011-01-01
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.
Benign-malignant mass classification in mammogram using edge weighted local texture features
Rabidas, Rinku; Midya, Abhishek; Sadhu, Anup; Chakraborty, Jayasree
2016-03-01
This paper introduces novel Discriminative Robust Local Binary Pattern (DRLBP) and Discriminative Robust Local Ternary Pattern (DRLTP) for the classification of mammographic masses as benign or malignant. Mass is one of the common, however, challenging evidence of breast cancer in mammography and diagnosis of masses is a difficult task. Since DRLBP and DRLTP overcome the drawbacks of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) by discriminating a brighter object against the dark background and vice-versa, in addition to the preservation of the edge information along with the texture information, several edge-preserving texture features are extracted, in this study, from DRLBP and DRLTP. Finally, a Fisher Linear Discriminant Analysis method is incorporated with discriminating features, selected by stepwise logistic regression method, for the classification of benign and malignant masses. The performance characteristics of DRLBP and DRLTP features are evaluated using a ten-fold cross-validation technique with 58 masses from the mini-MIAS database, and the best result is observed with DRLBP having an area under the receiver operating characteristic curve of 0.982.
Sato, Seichi; Kurihara, Toru; Ando, Shigeru
This paper proposes an exact direct method to determine all parameters including an envelope peak of the white-light interferogram. A novel mathematical technique, the weighted integral method (WIM), is applied that starts from the characteristic differential equation of the target signal, interferogram in this paper, to obtain the algebraic relation among the finite-interval weighted integrals (observations) of the signal and the waveform parameters (unknowns). We implemented this method using FFT and examined through various numerical simulations. The results show the method is able to localize the envelope peak very accurately even if it is not included in the observed interval. The performance comparisons reveal the superiority of the proposed algorithm over conventional algorithms in all terms of accuracy, efficiency, and estimation range.
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
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Leticia Sturlini Barticciotto
2016-06-01
Full Text Available The aim of this study was to investigate the effect of intratesticular local anesthesia on weight gain and behavioral and cortisol changes in piglets submitted to castration. Study design was a randomized controlled trial. Twenty-nine male Landrace crossbred piglets aged 3 to 6 days were used. The piglets were randomly divided into two groups: orchiectomy with anesthesia (A, n = 15 and orchiectomy without anesthesia (NOA, n = 14. Piglets in the A and NOA groups were treated with 0.5 mL of intratesticular 2% lidocaine with a vasoconstrictor in each testicle and 0.5 mL of intratesticular 0.9% sodium chloride, respectively. Blood samples were collected from all animals immediately after surgery and at 3 and 6 days postoperatively for measurement of the serum cortisol concentration. Behavior was assessed daily for 6 days by two observers blinded to the treatment. Weight was measured at the same time as cortisol measurement. Analysis of variance followed by the Student-Newman-Keuls test was used to investigate differences in time within and between the two groups. Mean times of six evaluations done for 90 minutes during 6 days of each behavior were compared by Tukey t test. The level of statistical significance was 5%. The serum cortisol concentration was higher immediately after surgery than at 3 and 6 days postoperatively in both groups. Weight gain was greater at 3 and 6 days postoperatively in the A group than in the NOA group. Intratesticular local anesthesia before castration in piglets is technically practical, is in accordance with good welfare practice, and improves short-term weight gain in piglets submitted to castration.
Bressan, Lucas P.; do Nascimento, Paulo Cícero; Schmidt, Marcella E. P.; Faccin, Henrique; de Machado, Leandro Carvalho; Bohrer, Denise
2017-02-01
A novel method was developed to determine low molecular weight polycyclic aromatic hydrocarbons in aqueous leachates from soils and sediments using a salting-out assisted liquid-liquid extraction, synchronous fluorescence spectrometry and a multivariate calibration technique. Several experimental parameters were controlled and the optimum conditions were: sodium carbonate as the salting-out agent at concentration of 2 mol L- 1, 3 mL of acetonitrile as extraction solvent, 6 mL of aqueous leachate, vortexing for 5 min and centrifuging at 4000 rpm for 5 min. The partial least squares calibration was optimized to the lowest values of root mean squared error and five latent variables were chosen for each of the targeted compounds. The regression coefficients for the true versus predicted concentrations were higher than 0.99. Figures of merit for the multivariate method were calculated, namely sensitivity, multivariate detection limit and multivariate quantification limit. The selectivity was also evaluated and other polycyclic aromatic hydrocarbons did not interfere in the analysis. Likewise, high performance liquid chromatography was used as a comparative methodology, and the regression analysis between the methods showed no statistical difference (t-test). The proposed methodology was applied to soils and sediments of a Brazilian river and the recoveries ranged from 74.3% to 105.8%. Overall, the proposed methodology was suitable for the targeted compounds, showing that the extraction method can be applied to spectrofluorometric analysis and that the multivariate calibration is also suitable for these compounds in leachates from real samples.
A Low Complexity System Based on Multiple Weighted Decision Trees for Indoor Localization
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David Sánchez-Rodríguez
2015-06-01
Full Text Available Indoor position estimation has become an attractive research topic due to growing interest in location-aware services. Nevertheless, satisfying solutions have not been found with the considerations of both accuracy and system complexity. From the perspective of lightweight mobile devices, they are extremely important characteristics, because both the processor power and energy availability are limited. Hence, an indoor localization system with high computational complexity can cause complete battery drain within a few hours. In our research, we use a data mining technique named boosting to develop a localization system based on multiple weighted decision trees to predict the device location, since it has high accuracy and low computational complexity. The localization system is built using a dataset from sensor fusion, which combines the strength of radio signals from different wireless local area network access points and device orientation information from a digital compass built-in mobile device, so that extra sensors are unnecessary. Experimental results indicate that the proposed system leads to substantial improvements on computational complexity over the widely-used traditional fingerprinting methods, and it has a better accuracy than they have.
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Megan A Carter
Full Text Available OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Suppression of noise in SEM images using weighted local hysteresis smoothing filter.
Mazhari, Mohadeseh; Hasanzadeh, Reza P R
2016-11-01
It has been proven that Hysteresis Smoothing (HS) has several advantages for Scanning Electron Microscopy (SEM) image noise reduction. HS uses hysteresis thresholding to remove noise besides preserving important details of images. Determination of optimal threshold values (cursor width) plays an effective role in improving the performance of HS based filters. Recently, a novel local technique, named Local Adaptive Hysteresis Smoothing (LAHS), has been proposed to compute an optimal cursor width. In this paper, a new method is proposed to improve the performance of LAHS in noise reduction and detail preservation. In the proposed approach which is based on weighted averaging, local statistical characteristics of the image are used in order to modify the final values of estimated pixels by LAHS method. Proposed method is applied to SEM images corrupted by different levels of noise. Noise reduction and detail preservation performance of the proposed method is compared in both objective and subjective manners with other HS based filters. Experimental results demonstrate that the proposed method is successful in improving the performance of LAHS and also it achieves better performance in noise reduction besides detail preservation of SEM images in comparison with other HS based filters. SCANNING 38:634-643, 2016. © 2016 Wiley Periodicals, Inc.
Zhang, Tianhao; Liu, Gang; Zhu, Zhongmin; Gong, Wei; Ji, Yuxi; Huang, Yusi
2016-01-01
The real-time estimation of ambient particulate matter with diameter no greater than 2.5 μm (PM2.5) is currently quite limited in China. A semi-physical geographically weighted regression (GWR) model was adopted to estimate PM2.5 mass concentrations at national scale using the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth product fused by the Dark Target (DT) and Deep Blue (DB) algorithms, combined with meteorological parameters. The fitting results could explain over 80% of the variability in the corresponding PM2.5 mass concentrations, and the estimation tends to overestimate when measurement is low and tends to underestimate when measurement is high. Based on World Health Organization standards, results indicate that most regions in China suffered severe PM2.5 pollution during winter. Seasonal average mass concentrations of PM2.5 predicted by the model indicate that residential regions, namely Jing-Jin-Ji Region and Central China, were faced with challenge from fine particles. Moreover, estimation deviation caused primarily by the spatially uneven distribution of monitoring sites and the changes of elevation in a relatively small region has been discussed. In summary, real-time PM2.5 was estimated effectively by the satellite-based semi-physical GWR model, and the results could provide reasonable references for assessing health impacts and offer guidance on air quality management in China. PMID:27706054
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Wolff, Hendrik Andreas; Herrmann, Markus Karl Alfred; Hennies, Steffen; Rave-Fraenk, Margret; Hess, Clemens Friedrich; Christiansen, Hans [Dept. of Radiotherapy and Radiooncology, Univ. Medicine Goettingen (Germany); Gaedcke, Jochen; Liersch, Torsten [Dept. of Surgery, Univ. Medicine Goettingen (Germany); Jung, Klaus [Dept. of Medical Statistics, Univ. Medicine Goettingen (Germany); Hermann, Robert Michael [Dept. of Radiotherapy and Radiooncology, Univ. Medicine Goettingen (Germany); Dept. of Radiotherapy and Radiooncology, Aerztehaus am Diako, Bremen (Germany); Rothe, Hilka [Dept. of Pathology, Univ. Medicine Goettingen (Germany); Schirmer, Markus [Dept. of Clinical Pharmacology, Univ. Medicine Goettingen (Germany)
2010-01-15
Purpose: To test for a possible correlation between high-grade acute organ toxicity during preoperative radiochemotherapy and complete tumor regression after total mesorectal excision in multimodal treatment of locally advanced rectal cancer. Patients and Methods: From 2001 to 2008, 120 patients were treated. Preoperative treatment consisted of normofractionated radiotherapy at a total dose of 50.4 Gy, and either two cycles of 5-fluorouracil (5-FU) or two cycles of 5-FU and oxaliplatin. Toxicity during treatment was monitored weekly, and any toxicity CTC (Common Toxicity Criteria) {>=} grade 2 of enteritis, proctitis or cystitis was assessed as high-grade organ toxicity for later analysis. Complete histopathologic tumor regression (TRG4) was defined as the absence of any viable tumor cells. Results: A significant coherency between high-grade acute organ toxicity and complete histopathologic tumor regression was found, which was independent of other factors like the preoperative chemotherapy schedule. The probability of patients with acute organ toxicity {>=} grade 2 to achieve TRG4 after neoadjuvant treatment was more than three times higher than for patients without toxicity (odds ratio: 3.29, 95% confidence interval: [1.01, 10.96]). Conclusion: Acute organ toxicity during preoperative radiochemotherapy in rectal cancer could be an early predictor of treatment response in terms of complete tumor regression. Its possible impact on local control and survival is under further prospective evaluation by the authors' working group. (orig.)
Directory of Open Access Journals (Sweden)
Muhdin .
2011-05-01
Full Text Available The compilation of growth stand model usually uses the regression analysis. Homoscedasticity or residual kind homogeneity is one assumption which underlying the use of this regression analysis. Breaking this assumption causes the low of model accuracy which is shown by the low of determination coefficient and the height of error standard. The problem of heteroscedasticity can be solved by using weighted regression analysis.The Selected Raiser Growth Model equation in this research was transformed into a model equation: ln P = a + b/A, where there was a significant correlation between the growth and the age (R2 = 55.04%, sb0 = 0.041, and sb1 = 0.171. From the use of weighted regression analysis with weightier wi = 1/”Xi, it can be concluded that there was no real correlation between the growth and the age (R2 = 0.55%, sb0 = 0.572, and sb1 = 2.560. The use of weightier shows much lower accuracy than without weightier. However, from the use of weighted regression analysis with weightier: wi = 1/si2, where si2 = residual kinds at free variable group to I (X1 shows that there was significant correlation between the growth and the age (R2 = 45.46%; sb0 = 0.084, and sb1 = 0.205. There fore it can be said that the accuracy was much better than regression without weightier. Furthermore, the use of weighted regression analysis with weightier wi = 1/si2, where si2 is residual kind at free variable to i (X which is estimated through second orde polynomial regression model shows a very significant correlation between the growth and the age (where R2 = 87.22%, sb0 = 0.029, and sb1 = 0.072. The last result shows a better accuracy than the preceding treatments. From this research, it can be concluded that by using a suitable weightier, the use of weighted regression analysis in compiling raiser growth model can improve the model accuracy. Keywords: growth model, weighted regression, acacia mangium,regression analysis
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Simona Bigerna
2015-08-01
Full Text Available In dealing with renewable electricity (RE, individuals are involved both as end-consumers on the demand side and as stakeholders (citizens in the local production process on the supply side. Empirical evidence shows that in many countries, consumers are willing to pay a significant amount to facilitate adoption of RE. By contrast, environmental externalities are often the cause of strong opposition to RE adoption if local communities are involved as stakeholders in wind, solar or biomass investment projects. Looking at the literature on willingness to pay and on willingness to accept, we have investigated RE acceptance mechanisms. First, we have used the meta-analysis to assess the major determinants of RE acceptance on both demand and supply sides. Meta-analysis has provided some insights useful for managing field research on an onshore wind farm enlargement project located in the Umbria region. Meta-analysis and survey results confirm that the local community plays a central role in local RE acceptance. Furthermore, people who have previous experience with windmills require less compensation, or are willing to pay more, for RE development. Results suggest that these attributes should be included in future research to improve understanding of determinants of RE acceptance.
Institute of Scientific and Technical Information of China (English)
LongShuyao; HuDe'an
2003-01-01
The meshless method is a new numerical technique presented in recent years .It uses the moving least square (MLS) approximation as a shape function . The smoothness of the MLS approximation is determined by that of the basic function and of the weight function, and is mainly determined by that of the weight function. Therefore, the weight function greatly affects the accuracy of results obtained. Different kinds of weight functions, such as the spline function, the Gauss function and so on, are proposed recently by many researchers. In the present work, the features of various weight functions are illustrated through solving elasto-static problems using the local boundary integral equation method. The effect of various weight functions on the accuracy, convergence and stability of results obtained is also discussed. Examples show that the weight function proposed by Zhou Weiyuan and Gauss and the quartic spline weight function are better than the others if parameters c and a in Gauss and exponential weight functions are in the range of reasonable values, respectively, and the higher the smoothness of the weight function, the better the features of the solutions.
XRA image segmentation using regression
Jin, Jesse S.
1996-04-01
Segmentation is an important step in image analysis. Thresholding is one of the most important approaches. There are several difficulties in segmentation, such as automatic selecting threshold, dealing with intensity distortion and noise removal. We have developed an adaptive segmentation scheme by applying the Central Limit Theorem in regression. A Gaussian regression is used to separate the distribution of background from foreground in a single peak histogram. The separation will help to automatically determine the threshold. A small 3 by 3 widow is applied and the modal of the local histogram is used to overcome noise. Thresholding is based on local weighting, where regression is used again for parameter estimation. A connectivity test is applied to the final results to remove impulse noise. We have applied the algorithm to x-ray angiogram images to extract brain arteries. The algorithm works well for single peak distribution where there is no valley in the histogram. The regression provides a method to apply knowledge in clustering. Extending regression for multiple-level segmentation needs further investigation.
You, Wei; Zang, Zengliang; Zhang, Lifeng; Li, Yi; Wang, Weiqi
2016-05-01
Taking advantage of the continuous spatial coverage, satellite-derived aerosol optical depth (AOD) products have been widely used to assess the spatial and temporal characteristics of fine particulate matter (PM2.5) on the ground and their effects on human health. However, the national-scale ground-level PM2.5 estimation is still very limited because the lack of ground PM2.5 measurements to calibrate the model in China. In this study, a national-scale geographically weighted regression (GWR) model was developed to estimate ground-level PM2.5 concentration based on satellite AODs, newly released national-wide hourly PM2.5 concentrations, and meteorological parameters. The results showed good agreements between satellite-retrieved and ground-observed PM2.5 concentration at 943 stations in China. The overall cross-validation (CV) R (2) is 0.76 and root mean squared prediction error (RMSE) is 22.26 μg/m(3) for MODIS-derived AOD. The MISR-derived AOD also exhibits comparable performance with a CV R (2) and RMSE are 0.81 and 27.46 μg/m(3), respectively. Annual PM2.5 concentrations retrieved either by MODIS or MISR AOD indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions especially for the regions without PMs monitoring sites.
WEIGHTED KOPPELMAN-LERAY-NORGUET FORMULAS ON A LOCAL q-CONCAVE WEDGE IN A COMPLEX MANIFOLD
Institute of Scientific and Technical Information of China (English)
邱春晖; 姚宗元
2003-01-01
A weighted Koppelman-Leray-Norguet formula of (r, s) differential forms ona local q-concave wedge in a complex manifold is obtained. By constructing the newweighted kernels, the authors give a new weighted Koppelman-Leray-Norguet formula with-out boundary integral of (r, s) differential forms, which is different from the classical one.The new weighted formula is especially suitable for the case of the local q-concave wedgewith a non-smooth boundary, so one can avoid complex estimates of boundary integralsand the density of integral may be not defined on the boundary but only in the domain.Moreover, the weighted integral formulas have much freedom in applications such as in theinterpolation of functions.
Conde, João; Oliva, Nuria; Zhang, Yi; Artzi, Natalie
2016-10-01
Conventional cancer therapies involve the systemic delivery of anticancer agents that neither discriminate between cancer and normal cells nor eliminate the risk of cancer recurrence. Here, we demonstrate that the combination of gene, drug and phototherapy delivered through a prophylactic hydrogel patch leads, in a colon cancer mouse model, to complete tumour remission when applied to non-resected tumours and to the absence of tumour recurrence when applied following tumour resection. The adhesive hydrogel patch enhanced the stability and provided local delivery of embedded nanoparticles. Spherical gold nanoparticles were used as a first wave of treatment to deliver siRNAs against Kras, a key oncogene driver, and rod-shaped gold nanoparticles mediated the conversion of near-infrared radiation into heat, causing the release of a chemotherapeutic as well as thermally induced cell damage. This local, triple-combination therapy can be adapted to other cancer cell types and to molecular targets associated with disease progression.
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.
Localization Methods of Weighted Centroid of dBZ on Weather-Radar Echo Maps in Vector Format
Directory of Open Access Journals (Sweden)
Xue-tao Yu
2013-02-01
Full Text Available Fast generation of weather-radar echo maps in vector format and accurate localization of weighted centroid of dBZ (dBZ stands for decibels of reflectivity of a radar signal reflected off a remote object are the basis of studying the characteristic tracking algorithms which are based on the vector echoes. The authors principally studied the approach to generating the vector echo map, and discussed the localization methods of weighted centroid of dBZ on vector echo maps. First, based on the traditional calculation method on raster echo data, some new localization methods of weighted centroid of dBZ on vector echo data were proposed by considering the weights of features’ area and distance from their location to radar center. Second, taking the base reflectivity products of CINRAD/SA weather radar in Meizhou city of China as data sources, they illustrated the storage structure of this type of echo data and studied the drawing mode of changing this type of data into vector format files under the polar coordinate system in detail. Third, using the same vector echo maps created by the above method, the weighted centroid of the same area was calculated by the above localization methods. In the end, Compared with the calculated value of the same area by traditional method which is based on raster echo maps, the three new calculated results and the sources of error were analyzed in detail and two conclusions were drawn: the echo’s precision in vector format is much higher than that in raster format and it is more accurate to take the features’ area and distance to radar center as weights during the calculation of weighted centroid of dBZ on echo maps in vector format.
Domagalski, J. L.; Schlegel, B.; Hutchins, J.
2014-12-01
Long-term data sets on stream-water quality and discharge can be used to assess whether best management practices (BMPs) are restoring beneficial uses of impaired water as required under the Clean Water Act. In this study, we evaluated a greater than 20-year record of water quality from selected streams in the Central Valley (CV) of California and Lake Tahoe (California and Nevada, USA). The CV contains a mix of agricultural and urbanized land, while the Lake Tahoe area is mostly forested, with seasonal residents and tourism. Because nutrients and fine sediments cause a reduction in water clarity that impair Lake Tahoe, BMPs were implemented in the early 1990's, to reduce nitrogen and phosphorus loads. The CV does not have a current nutrient management plan, but numerous BMPs exist to reduce pesticide loads, and it was hypothesized that these programs could also reduce nutrient levels. In the CV and Lake Tahoe areas, nutrient concentrations, loads, and trends were estimated by using the recently developed Weighted Regressions on Time, Discharge, and Season (WRTDS) model. Sufficient data were available to compare trends during a voluntary and enforcement period for seven CV sites within the lower Sacramento and San Joaquin Basins. For six of the seven sites, flow-normalized mean annual concentrations of total phosphorus and nitrate decreased at a faster rate during the enforcement period than during the earlier voluntary period. Concentration changes during similar years and ranges of flow conditions suggest that BMPs designed for pesticides also reduced nutrient loads in the CV. A trend analysis using WRTDS was completed for six streams that enter Lake Tahoe during the late 1980's through 2008. The results of the model confirm that nutrient loading is influenced strongly by season, such as by spring runoff from snowmelt. The highest nutrient concentrations in the late 1980's and early 1990's correlate with high flows, followed by statistically significant decreases
Riley, Richard D; Ensor, Joie; Jackson, Dan; Burke, Danielle L
2017-01-01
Many meta-analysis models contain multiple parameters, for example due to multiple outcomes, multiple treatments or multiple regression coefficients. In particular, meta-regression models may contain multiple study-level covariates, and one-stage individual participant data meta-analysis models may contain multiple patient-level covariates and interactions. Here, we propose how to derive percentage study weights for such situations, in order to reveal the (otherwise hidden) contribution of each study toward the parameter estimates of interest. We assume that studies are independent, and utilise a decomposition of Fisher's information matrix to decompose the total variance matrix of parameter estimates into study-specific contributions, from which percentage weights are derived. This approach generalises how percentage weights are calculated in a traditional, single parameter meta-analysis model. Application is made to one- and two-stage individual participant data meta-analyses, meta-regression and network (multivariate) meta-analysis of multiple treatments. These reveal percentage study weights toward clinically important estimates, such as summary treatment effects and treatment-covariate interactions, and are especially useful when some studies are potential outliers or at high risk of bias. We also derive percentage study weights toward methodologically interesting measures, such as the magnitude of ecological bias (difference between within-study and across-study associations) and the amount of inconsistency (difference between direct and indirect evidence in a network meta-analysis).
Hao, Lingxin
2007-01-01
Quantile Regression, the first book of Hao and Naiman's two-book series, establishes the seldom recognized link between inequality studies and quantile regression models. Though separate methodological literature exists for each subject, the authors seek to explore the natural connections between this increasingly sought-after tool and research topics in the social sciences. Quantile regression as a method does not rely on assumptions as restrictive as those for the classical linear regression; though more traditional models such as least squares linear regression are more widely utilized, Hao
Li, Jiazhong; Li, Shuyan; Lei, Beilei; Liu, Huanxiang; Yao, Xiaojun; Liu, Mancang; Gramatica, Paola
2010-04-15
In the quantitative structure-activity relationship (QSAR) study, local lazy regression (LLR) can predict the activity of a query molecule by using the information of its local neighborhood without need to produce QSAR models a priori. When a prediction is required for a query compound, a set of local models including different number of nearest neighbors are identified. The leave-one-out cross-validation (LOO-CV) procedure is usually used to assess the prediction ability of each model, and the model giving the lowest LOO-CV error or highest LOO-CV correlation coefficient is chosen as the best model. However, it has been proved that the good statistical value from LOO cross-validation appears to be the necessary, but not the sufficient condition for the model to have a high predictive power. In this work, a new strategy is proposed to improve the predictive ability of LLR models and to access the accuracy of a query prediction. The bandwidth of k neighbor value for LLR is optimized by considering the predictive ability of local models using an external validation set. This approach was applied to the QSAR study of a series of thienopyrimidinone antagonists of melanin-concentrating hormone receptor 1. The obtained results from the new strategy shows evident improvement compared with the commonly used LOO-CV LLR methods and the traditional global linear model. 2009 Wiley Periodicals, Inc.
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Cao, Kun, E-mail: kun-cao@hotmail.com [Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 52 Fucheng Road, Haidian District, Beijing 100142 (China); Gao, Min, E-mail: gaominmin202@163.com [Department of Gynecology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 52 Fucheng Road, Haidian District, Beijing 100142 (China); Sun, Ying-Shi, E-mail: sunysabc@163.com [Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 52 Fucheng Road, Haidian District, Beijing 100142 (China); Li, Yan-Ling, E-mail: yanlingli1982@gmail.com [Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 52 Fucheng Road, Haidian District, Beijing 100142 (China); Sun, Yu, E-mail: sunyu_bch@163.com [Department of Pathology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 52 Fucheng Road, Haidian District, Beijing 100142 (China); Gao, Yu-Nong, E-mail: gaoyunong@vip.sina.com [Department of Gynecology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 52 Fucheng Road, Haidian District, Beijing 100142 (China); Zhang, Xiao-Peng, E-mail: zxp@bjcancer.org [Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, 52 Fucheng Road, Haidian District, Beijing 100142 (China)
2012-08-15
Objective: To evaluate the relationship between apparent diffusion coefficient (ADC) value and the local invasiveness of endometrial carcinoma. Methods and materials: The MR imaging of seventy-three patients with endometrial carcinoma proved by post-operative pathology and sixty-four patients with healthy uteri were retrospectively reviewed. All MR examinations included axial T2WI and T1WI, sagittal T2WI and diffusion-weighted sequences (b = 0 and b = 1000 s/mm{sup 2}). Tumor size, mean ADC value (ADCm) and quartile ADC (ADCq) were acquired on post-processing workstation using voxel-analysis software. Differences between the ADC values among three layers of normal uterine body and endometrial carcinomas were compared by ANOVA test. Groups were divided according to pathologic type, histologic grade, depth of myometrial infiltration, presence of cervical invasion and lymphovascular space invasion, and lymph node metastasis. Tumor size and ADC values were compared and analyzed. Results: ADC values were different in three zones of uterine body (P < 0.001), with the lowest in junctional zone [(1.126 {+-} 0.190) Multiplication-Sign 10{sup -3} mm{sup 2}/s] and highest in outer myometrium [(1.496 {+-} 0.196) Multiplication-Sign 10{sup -3} mm{sup 2}/s]. Mean ADC value of endometrial carcinomas [(1.011 {+-} 0.121) Multiplication-Sign 10{sup -3} mm{sup 2}/s] was lower than the normal uterine body. Quartile ADC and tumor size were greater in groups with more invasive pathologic factors (P < 0.05). Deep myometrial infiltration, cervical invasion, lymphovascular space invasion and lymph node metastasis were more common as quartile ADC values and tumor sizes increased. Conclusion: Mean ADC value was lower in endometrial carcinoma was lower than the normal uterus. Quartile ADC, representing the intra-tumor heterogeneity of water movement, had a profound relationship with invasiveness of endometrial carcinomas, while mean ADC value did not. ADC values may serve as a quantitative
Zhu, Ke; 10.1214/11-AOS895
2012-01-01
This paper investigates the asymptotic theory of the quasi-maximum exponential likelihood estimators (QMELE) for ARMA--GARCH models. Under only a fractional moment condition, the strong consistency and the asymptotic normality of the global self-weighted QMELE are obtained. Based on this self-weighted QMELE, the local QMELE is showed to be asymptotically normal for the ARMA model with GARCH (finite variance) and IGARCH errors. A formal comparison of two estimators is given for some cases. A simulation study is carried out to assess the performance of these estimators, and a real example on the world crude oil price is given.
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 patientsÃ¢Â€Â™ 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.
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.
Institute of Scientific and Technical Information of China (English)
Kang Guo; Lan-Jun Zhang; Ling Cai; Yu Zhang; Jian-Fei Zhu; Tie-Hua Rong; Peng Lin; Chong-Li Hao; Wu-Ping Wang; Zhe Li
2012-01-01
Response criteria remain controversial in therapeutic evaluation for locally advanced esophageal carcinoma treated with neoadjuvant chemotherapy.We aimed to identify the predictive value of tumor regression grading (TRG) in tumor response and prognosis.Fifty-two patients who underwent neoadjuvant chemotherapy followed by esophagectomy and radical 2-field lymphadenectomy between June 2007 and June 2011 were included in this study.All tissue specimens were reassessed according to the TRG scale.Potential prognostic factors,including clinicopathologic factors,were evaluated.Survival curves were generated by using the Kaplan-Meier method and compared with the log-rank test.Prognostic factors were determined with multivariate analysis by using the Cox regression model.Our results showed that of 52 cases,43 (83％) were squamous cell carcinoma and 9 (17％) were adenocarcinoma.TRG was correlated with pathologic T (P =0.006) and N (P ＜ 0.001) categories.Median overall survival for the entire cohort was 33 months.The 1- and 2-year overall survival rates were 71％ and 44％,respectively.Univariate survival analysis results showed that favorable prognostic factors were histological subtype (P =0.003),pathologic T category (P =0.026),pathologic N category (P ＜ 0.001),and TRG G0 (P =0.041).Multivariate analyses identified pathologic N category (P ＜ 0.001) as a significant independent prognostic parameter.Our results indicate that histomorphologic TRG can be considered as an alternative option to predict the therapeutic efficacy and prognostic factor for patients with locally advanced esophageal carcinoma treated by neoadjuvant chemotherapy.
Deng, Xiaogang; Wang, Lei
2017-10-07
Traditional kernel principal component analysis (KPCA) based nonlinear process monitoring method may not perform well because its Gaussian distribution assumption is often violated in the real industrial processes. To overcome this deficiency, this paper proposes a modified KPCA method based on double-weighted local outlier factor (DWLOF-KPCA). In order to avoid the assumption of specific data distribution, local outlier factor (LOF) is introduced to construct two LOF-based monitoring statistics, which are used to substitute for the traditional T(2) and SPE statistics, respectively. To provide better online monitoring performance, a double-weighted LOF method is further designed, which assigns the weights for each component to highlight the key components with significant fault information, and uses the moving window to weight the historical statistics for reducing the drastic fluctuations in the monitoring results. Finally, simulations on a numerical example and the Tennessee Eastman (TE) benchmark process are used to demonstrate the superiority of the proposed DWLOF-KPCA method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Kahane, Leo H
2007-01-01
Using a friendly, nontechnical approach, the Second Edition of Regression Basics introduces readers to the fundamentals of regression. Accessible to anyone with an introductory statistics background, this book builds from a simple two-variable model to a model of greater complexity. Author Leo H. Kahane weaves four engaging examples throughout the text to illustrate not only the techniques of regression but also how this empirical tool can be applied in creative ways to consider a broad array of topics. New to the Second Edition Offers greater coverage of simple panel-data estimation:
DEFF Research Database (Denmark)
Haack, Søren; Tanderup, Kari; Fokdal, Lars
Diffusion weighted MRI has shown great potential in diagnostic cancer imaging and may also have value for monitoring tumor response during radiotherapy. Patients with advanced cervical cancer are treated with external beam radiotherapy followed by brachytherapy. This study evaluates the value of ...
DEFF Research Database (Denmark)
Haack, Søren; Tanderup, Kari; Fokdal, Lars;
Diffusion weighted MRI has shown great potential in diagnostic cancer imaging and may also have value for monitoring tumor response during radiotherapy. Patients with advanced cervical cancer are treated with external beam radiotherapy followed by brachytherapy. This study evaluates the value of ...
Jacobsen, R. T.; Stewart, R. B.; Crain, R. W., Jr.; Rose, G. L.; Myers, A. F.
1976-01-01
A method was developed for establishing a rational choice of the terms to be included in an equation of state with a large number of adjustable coefficients. The methods presented were developed for use in the determination of an equation of state for oxygen and nitrogen. However, a general application of the methods is possible in studies involving the determination of an optimum polynomial equation for fitting a large number of data points. The data considered in the least squares problem are experimental thermodynamic pressure-density-temperature data. Attention is given to a description of stepwise multiple regression and the use of stepwise regression in the determination of an equation of state for oxygen and nitrogen.
Weighted Least Squares Techniques for Improved Received Signal Strength Based Localization
Casar, José R.; Bernardos, Ana M.; Paula Tarrío
2011-01-01
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...
Domingos-Pereira, S; Decrausaz, L; Derré, L; Bobst, M; Romero, P; Schiller, J T; Jichlinski, P; Nardelli-Haefliger, D
2013-03-01
Human papillomaviruses (HPV)-related cervical cancer is the second leading cause of cancer death in women worldwide. Despite active development, HPV E6/E7 oncogene-specific therapeutic vaccines have had limited clinical efficacy to date. Here, we report that intravaginal (IVAG) instillation of CpG-ODN (TLR9 agonist) or poly-(I:C) (TLR3 agonist) after subcutaneous E7 vaccination increased ~fivefold the number of vaccine-specific interferon-γ-secreting CD8 T cells in the genital mucosa (GM) of mice, without affecting the E7-specific systemic response. The IVAG treatment locally increased both E7-specific and total CD8 T cells, but not CD4 T cells. This previously unreported selective recruitment of CD8 T cells from the periphery by IVAG CpG-ODN or poly-(I:C) was mediated by TLR9 and TLR3/melanoma differentiation-associated gene 5 signaling pathways, respectively. For CpG, this recruitment was associated with a higher proportion of GM-localized CD8 T cells expressing both CCR5 and CXCR3 chemokine receptors and E-selectin ligands. Most interestingly, IVAG CpG-ODN following vaccination led to complete regression of large genital HPV tumors in 75% of mice, instead of 20% with vaccination alone. These findings suggest that mucosal application of immunostimulatory molecules might substantially increase the effectiveness of parenterally administered vaccines.
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 pollution in epidemiologic birth outcome studies.
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
Energy Technology Data Exchange (ETDEWEB)
Diez, Reinaldo Pis [CEQUINOR, Centro de Quimica Inorganica (CONICET, UNLP), Departamento de Quimica, Facultad de Ciencias Exactas, UNLP CC 962, B1900AVV La Plata (Argentina); Karasiev, Valentin V [Centro de Qimica, Instituto Venezolano de Investigaciones Cientificas, IVIC, Apartado 21827, Caracas 1020-A (Venezuela)
2003-07-14
A relationship between the auxiliary density, {rho}(r), defined within the framework of the weighted density approximation and the kinetic energy modulating factor, A{sub N}([{rho}(r)]; r), which appears in the local-scaling transformation version of density functional theory is presented. This relationship imposes the condition of positiveness on the kinetic energy modulating factor and this, in turn, leads to an important mathematical condition on any approximate kinetic energy density functional. It is shown that two well-known approximate kinetic energy density functionals do not satisfy the above relationship at distances very close to the nucleus. By forcing a given approximate kinetic energy density functional to obey the above condition, both the kinetic and exchange energies can be obtained within a framework similar to that of the weighted density approximation. Results on some closed-shell atomic systems provide support for those ideas.
Matson, Johnny L.; Kozlowski, Alison M.
2010-01-01
Autistic regression is one of the many mysteries in the developmental course of autism and pervasive developmental disorders not otherwise specified (PDD-NOS). Various definitions of this phenomenon have been used, further clouding the study of the topic. Despite this problem, some efforts at establishing prevalence have been made. The purpose of…
Nick, Todd G; Campbell, Kathleen M
2007-01-01
The Medical Subject Headings (MeSH) thesaurus used by the National Library of Medicine defines logistic regression models as "statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable." Logistic regression models are used to study effects of predictor variables on categorical outcomes and normally the outcome is binary, such as presence or absence of disease (e.g., non-Hodgkin's lymphoma), in which case the model is called a binary logistic model. When there are multiple predictors (e.g., risk factors and treatments) the model is referred to as a multiple or multivariable logistic regression model and is one of the most frequently used statistical model in medical journals. In this chapter, we examine both simple and multiple binary logistic regression models and present related issues, including interaction, categorical predictor variables, continuous predictor variables, and goodness of fit.
Enticott, Joanne C; Cheng, I-Hao; Russell, Grant; Szwarc, Josef; Braitberg, George; Peek, Anne; Meadows, Graham
2015-01-01
This study investigated if people born in refugee source countries are disproportionately represented among those receiving a diagnosis of mental illness within emergency departments (EDs). The setting was the Cities of Greater Dandenong and Casey, the resettlement region for one-twelfth of Australia's refugees. An epidemiological, secondary data analysis compared mental illness diagnoses received in EDs by refugee and non-refugee populations. Data was the Victorian Emergency Minimum Dataset in the 2008-09 financial year. Univariate and multivariate logistic regression created predictive models for mental illness using five variables: age, sex, refugee background, interpreter use and preferred language. Collinearity, model fit and model stability were examined. Multivariate analysis showed age and sex to be the only significant risk factors for mental illness diagnosis in EDs. 'Refugee status', 'interpreter use' and 'preferred language' were not associatedwith a mental health diagnosis following risk adjustment forthe effects ofage and sex. The disappearance ofthe univariate association after adjustment for age and sex is a salutary lesson for Medicare Locals and other health planners regarding the importance of adjusting analyses of health service data for demographic characteristics.
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.
Weighted sparse representation for human ear recognition based on local descriptor
Mawloud, Guermoui; Djamel, Melaab
2016-01-01
A two-stage ear recognition framework is presented where two local descriptors and a sparse representation algorithm are combined. In a first stage, the algorithm proceeds by deducing a subset of the closest training neighbors to the test ear sample. The selection is based on the K-nearest neighbors classifier in the pattern of oriented edge magnitude feature space. In a second phase, the co-occurrence of adjacent local binary pattern features are extracted from the preselected subset and combined to form a dictionary. Afterward, sparse representation classifier is employed on the developed dictionary in order to infer the closest element to the test sample. Thus, by splitting up the ear image into a number of segments and applying the described recognition routine on each of them, the algorithm finalizes by attributing a final class label based on majority voting over the individual labels pointed out by each segment. Experimental results demonstrate the effectiveness as well as the robustness of the proposed scheme over leading state-of-the-art methods. Especially when the ear image is occluded, the proposed algorithm exhibits a great robustness and reaches the recognition performances outlined in the state of the art.
Olive, David J
2017-01-01
This text covers both multiple linear regression and some experimental design models. The text uses the response plot to visualize the model and to detect outliers, does not assume that the error distribution has a known parametric distribution, develops prediction intervals that work when the error distribution is unknown, suggests bootstrap hypothesis tests that may be useful for inference after variable selection, and develops prediction regions and large sample theory for the multivariate linear regression model that has m response variables. A relationship between multivariate prediction regions and confidence regions provides a simple way to bootstrap confidence regions. These confidence regions often provide a practical method for testing hypotheses. There is also a chapter on generalized linear models and generalized additive models. There are many R functions to produce response and residual plots, to simulate prediction intervals and hypothesis tests, to detect outliers, and to choose response trans...
Local and global aspects of biological motion perception in children born at very low birth weight.
Williamson, K E; Jakobson, L S; Saunders, D R; Troje, N F
2015-01-01
Biological motion perception can be assessed using a variety of tasks. In the present study, 8- to 11-year-old children born prematurely at very low birth weight (body structure, and the ability to carry out higher order processes required for action recognition and person identification. Preterm children exhibited difficulties in all 4 aspects of biological motion perception. However, intercorrelations between test scores were weak in both full-term and preterm children--a finding that supports the view that these processes are relatively independent. Preterm children also displayed more autistic-like traits than full-term peers. In preterm (but not full-term) children, these traits were negatively correlated with performance in the task requiring structure-from-motion processing, r(30) = -.36, p children and suggest that a core deficit in social perception/cognition may contribute to the development of the social and behavioral difficulties even in members of this population who are functioning within the normal range intellectually. The results could inform the development of screening, diagnostic, and intervention tools.
FUNCTIONAL-COEFFICIENT REGRESSION MODEL AND ITS ESTIMATION
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
In this paper,a class of functional-coefficient regression models is proposed and an estimation procedure based on the locally weighted least equares is suggested. This class of models,with the proposed estimation method,is a powerful means for exploratory data analysis.
Golestaneh, S Alireza; Karam, Lina
2016-08-24
Perceptual image quality assessment (IQA) attempts to use computational models to estimate the image quality in accordance with subjective evaluations. Reduced-reference (RR) image quality assessment (IQA) methods make use of partial information or features extracted from the reference image for estimating the quality of distorted images. Finding a balance between the number of RR features and accuracy of the estimated image quality is essential and important in IQA. In this paper we propose a training-free low-cost RRIQA method that requires a very small number of RR features (6 RR features). The proposed RRIQA algorithm is based on the discrete wavelet transform (DWT) of locally weighted gradient magnitudes.We apply human visual system's contrast sensitivity and neighborhood gradient information to weight the gradient magnitudes in a locally adaptive manner. The RR features are computed by measuring the entropy of each DWT subband, for each scale, and pooling the subband entropies along all orientations, resulting in L RR features (one average entropy per scale) for an L-level DWT. Extensive experiments performed on seven large-scale benchmark databases demonstrate that the proposed RRIQA method delivers highly competitive performance as compared to the state-of-the-art RRIQA models as well as full reference ones for both natural and texture images. The MATLAB source code of REDLOG and the evaluation results are publicly available online at https://http://lab.engineering.asu.edu/ivulab/software/redlog/.
Institute of Scientific and Technical Information of China (English)
Ru-Hua SONG; Daimaru HIROMU; Abe KAZUTOKI; Kurokawa USIO; Matsuura SUMIO
2008-01-01
A number of statistical methods are typically used to effectively predict potential landslide distributions.In this study two multivariate statistical analysis methods were used (weights of evidence and logistic regression) to predict the potential distribution of shallow-seated landslides in the Kamikawachi area of Sabae City,Fukui Prefecture,Japan.First,the dependent variable (shallow-seated landslides) was divided into presence and absence,and the independent variables (environmental factors such as slope and altitude) were categorized according to their characteristics.Then,using the weights of evidence (WE) method,the weights of pairs comprising presence (w+(i) ) or absence (w-(i) ),and the contrast values for each category of independent variable (evidence),were calculated.Using the method that integrated the weights of evidence method and a logistic regression model,score values were calculated for each category of independent variable.Based on these contrast values,three models were selected to sum the score values of every gird in the study area.According to a receiver operating characteristic curve analysis (ROC),model 2 yielded the best fit for predicting the potential distribution of shallow-seated landslide hazards,with 89% correctness and a 54.5% hit ratio when the occurrence probability (OP) of landslides was 70%.The model was tested using data from an area close to the study region,and showed 94% correctness and a hit ratio of 45.7% when the OP of landslides was 70%.Finally,the potential distribution of shallow-seated landslides,based on the OP,was mapped using a geographical information system.
Institute of Scientific and Technical Information of China (English)
Yizhao Li; Guixiang Cui; Qingde Wang; Hongxia Liu; Xiaoxia Zhang; Fengshan Wang; Keqin Xie
2009-01-01
BACKGROUND: Studies have shown that low molecular weight heparin-superoxide dismutase conjugate exhibits a remarkable neuroprotective effect.OBJECTIVE: To investigate the effect of low molecular weight heparin-superoxide dismutase conjugate on astrocytes in an interleukin-6 (IL-6) overexpressing mice following local cerebral ischemia.DESIGN, TIME AND SETTING: Randomized, cytological, controlled, animal study was performed in the Department of Physiology and Neuroscience, Neurology and Biochemistry and Molecular Biology, Medical University of South Carolina from January 2005 to March 2005.MATERIALS: Nine IL-6 transgenic mice, irrespective of gender, were randomly divided into three groups: sham-operated, model, and treatment, with three mice in each group. With exception of the sham-operated group, right middle cerebral artery occlusion was induced in the mice.Expression of glial fibrillary acidic protein, an astrocyte marker, was determined by immunohistochemistry. Low molecular weight heparin-superoxide dismutase conjugate was purchased from Biochemistry and Biotechnique Institute, Shandong University.METHODS: Two minutes prior to ischemia induction, 0.5 mL/kg saline or 20 000 U/kg low molecular weight heparin-superoxicle dismutase conjugate were administrated via the femoral artery in the model group and treatment group, respectively. The sham-operated group underwent the same protocols, with the exception of occlusion and treatment.MAIN OUTCOME MEASURES: The number of glial fibrillary acidic protein-positive cells was quantified under light microscopy (x200).RESULTS: In the sham-operated group, there were a large number of astrocytes in the IL-6 transgenic mice. However, the cell bodies were small, and the branches were few and thin. The number of astrocytes in the model group was remarkably less than the sham-operated group. Compared to the model and sham-operated groups, the number of astrocytes significantly increased, and the cell body became larger
Institute of Scientific and Technical Information of China (English)
赵付洲; 宋冰; 侍洪波
2016-01-01
There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization (WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description (SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method’s validity, it is applied to a numerical example and a Tennessee Eastman (TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy (LNS-PCA) in multi-mode process monitoring.
Institute of Scientific and Technical Information of China (English)
刘海涛; 魏汝祥; 蒋国萍
2012-01-01
For the excessiveness of operating factors and multi-correlation of variables in software cost estimation, this paer proposes a software cost estimation method based on Partial Least Squares Regression(PLSR). After the variables are weighted, an integrated index named analogue deviation is defined to describe the approximation of data samples. Then an adaptive weight is assigned to sample according to the approximation, and the optimal partial least squares latent variables and weight parameters are calculated by traversal searching. Experimental results show that the prediction error is reduced by 73.61% and 32.34% than multiple linear regression and PLSR respectively.%针对软件成本估算中影响因素较多且自变量间存在多重相关性的特点,提出一种基于加权偏最小二乘回归(PLSR)的软件成本估算方法.定义属性权重,得到描述软件历史数据相似度的加权相似离度.通过计算样本相似度自适应地为样本分配权值,采用遍历搜索的方式确定最优主成分及权值分配参数.实验结果表明,该方法的估算误差比多元线性回归方法减少73.61％,比全局PLSR方法减少32.34％.
Carmichael, A R; Ninkovic, G; Boparai, R
2004-08-01
The ultimate goal of breast conserving surgery (BCS) is to achieve survival and local control rates similar to those for mastectomy while providing improved cosmetic and functional results. The volume of breast tissue removed is the most significant determinant of the final cosmetic outcome of BCS. We hypothesised that intra-operative specimen radiograph (IOSR) during BCS may guide the surgeon to achieve clear radiographic and histological margins with minimum normal breast tissue excision, thus preserving cosmetic appearance. The aim of this study was to evaluate the effect of introducing the policy of IOSR on the weight of specimens of wide local excision of palpable invasive breast cancer. All consecutive patients who underwent therapeutic wide local excision for palpable invasive breast cancer from 01/01/02 to 31/03/03 were included in this study. A policy of IOSR was introduced in October 2002, thus all BCS done after 01/10/2002 underwent IOSR. The mean (S.D.) specimen weight for the no intra-operative specimen radiograph (NIOSR) group was 74 grams compared to 46 g in the IOSR group, (P = 0.0241, unpaired t-test with Welch's correction) and the mean tumour size for the NIOSR was 23(13)mm and for IOSR was 21(8)mm (P = ns, unpaired t-test with Welch's correction). A histologically clear circumferential margin rate in the IOSR group was 96% compared to 82% in the NIOSR group. Five patients in the IOSR group and 11 in NIOSR group had positive anterior or posterior margin. For these patients no further surgical excision was possible as BCS was performed from skin to pectoral fascia. Therefore a radiation boost was given to the site of excision. Only one patient in the IOSR group needed further breast surgery (mastectomy) for a positive inferior (toward nipple) margin for a mammographically occult tumour, while 11 patients in the NIOSR group required further breast surgery. In conclusion, IOSR is a simple, effective and economical way of assessing adequacy of
DEFF Research Database (Denmark)
Galsgaard, Bo; Lundtoft, Dennis Holm; Nikolov, Ivan Adriyanov;
2015-01-01
One of the time consuming tasks in the timber industry is the manually measurement of features of wood stacks. Such features include, but are not limited to, the number of the logs in a stack, their diameters distribution, and their volumes. Computer vision techniques have recently been used...... for solving this real-world industrial application. Such techniques are facing many challenges as the task is usually performed in outdoor, uncontrolled, environments. Furthermore, the logs can vary in texture and they can be occluded by different obstacles. These all make the segmentation of the wood logs...... about the foreand background regions of a stack image, and then use this together with a Local Circularity Measure (LCM) to modify the weights of the graph to segment the wood logs from the rest of the image. We further improve the segmentation by separating overlapping logs. These segmented wood logs...
Energy Technology Data Exchange (ETDEWEB)
Rouviere, Olivier; Lyonnet, Denis [Hopital Edouard Herriot, Hospices Civils de Lyon, Department of Urinary and Vascular Radiology, Lyon (France); Universite de Lyon, Lyon (France); Universite de Lyon 1, Faculte de medecine Lyon Nord, Lyon (France); Inserm, U556, Lyon (France); Girouin, Nicolas; Glas, Ludivine; Ben Cheikh, Alexandre [Hopital Edouard Herriot, Hospices Civils de Lyon, Department of Urinary and Vascular Radiology, Lyon (France); Universite de Lyon, Lyon (France); Universite de Lyon 1, Faculte de medecine Lyon Nord, Lyon (France); Gelet, Albert [Hopital Edouard Herriot, Hospices Civils de Lyon, Department of Urology, Lyon (France); Inserm, U556, Lyon (France); Mege-Lechevallier, Florence [Hopital Edouard Herriot, Hospices Civils de Lyon, Department of Pathology, Lyon (France); Rabilloud, Muriel [Hospices Civils de Lyon, Department of Biostatistics, Lyon (France); Universite de Lyon 1, UMR CNRS, Laboratoire Biostatistiques-Sante, Pierre-Benite (France); Chapelon, Jean-Yves [Inserm, U556, Lyon (France)
2010-01-15
The objective was to evaluate T2-weighted (T2w) and dynamic contrast-enhanced (DCE) MRI in detecting local cancer recurrences after prostate high-intensity focused ultrasound (HIFU) ablation. Fifty-nine patients with biochemical recurrence after prostate HIFU ablation underwent T2-weighted and DCE MRI before transrectal biopsy. For each patient, biopsies were performed by two operators: operator 1 (blinded to MR results) performed random and colour Doppler-guided biopsies (''routine biopsies''); operator 2 obtained up to three cores per suspicious lesion on MRI (''targeted biopsies''). Seventy-seven suspicious lesions were detected on DCE images (n=52), T2w images (n=2) or both (n=23). Forty patients and 41 MR lesions were positive at biopsy. Of the 36 remaining MR lesions, 20 contained viable benign glands. Targeted biopsy detected more cancers than routine biopsy (36 versus 27 patients, p=0.0523). The mean percentages of positive cores per patient and of tumour invasion of the cores were significantly higher for targeted biopsies (p<0.0001). The odds ratios of the probability of finding viable cancer and viable prostate tissue (benign or malignant) at targeted versus routine biopsy were respectively 3.35 (95% CI 3.05-3.64) and 1.38 (95% CI 1.13-1.63). MRI combining T2-weighted and DCE images is a promising method for guiding post-HIFU biopsy towards areas containing recurrent cancer and viable prostate tissue. (orig.)
Energy Technology Data Exchange (ETDEWEB)
Sakai, Yuki [Research Faculty of Agriculture, Hokkaido University, Kita 9, Nishi 9, Kitaku, Sapporo 060-8589 (Japan); Watanabe, Toshihiro, E-mail: nabe@chem.agr.hokudai.ac.j [Research Faculty of Agriculture, Hokkaido University, Kita 9, Nishi 9, Kitaku, Sapporo 060-8589 (Japan); Wasaki, Jun [Graduate School of Biosphere Science, Hiroshima University, Kagamiyama 1-7-1, Higashi-Hiroshima 739-8521 (Japan); Senoura, Takeshi [Graduate School of Agricultural and Life Sciences, University of Tokyo, 1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657 (Japan); Shinano, Takuro [National Agricultural Research Center for Hokkaido Region, Sapporo 062-8555 (Japan); Osaki, Mitsuru [Research Faculty of Agriculture, Hokkaido University, Kita 9, Nishi 9, Kitaku, Sapporo 060-8589 (Japan)
2010-12-15
The roles of low-molecular-weight thiols (LMWTs), such as glutathione and phytochelatins, in arsenic (As) tolerance and hyperaccumulation in Pteris vittata an As-hyperaccumulator fern remain to be better understood. This study aimed to thoroughly characterize LMWT synthesis in P. vittata to understand the roles played by LMWTs in As tolerance and hyperaccumulation. LMWT synthesis in P. vittata was induced directly by As, and not by As-mediated oxidative stress. Expression of PvECS2, one of the putative genes of {gamma}-glutamylcysteine synthetase ({gamma}ECS), increases in P. vittata shoots at 48 h after the onset of As exposure, almost corresponding to the increase in the concentrations of {gamma}-glutamylcysteine and glutathione. Furthermore, localization of As showed similar trends to those of LMWTs in fronds at both whole-frond and cellular levels. This study thus indicates the specific contribution of LMWTs to As tolerance in P. vittata. {gamma}ECS may be responsible for the As-induced enhancement of LMWT synthesis. - Pteris vittata specifically enhances low-molecular-weight thiol synthesis against arsenic toxicities
Hop Weighted DV-Hop Localization Algorithm%跳数加权DV-Hop定位算法
Institute of Scientific and Technical Information of China (English)
刘凯; 余君君; 谭立雄
2012-01-01
A beacon node selection scheme and a DV-Hop localization algorithm based on hop weighted are proposed to improve the positioning performance in this paper. First of all, by setting a threshold value of signal propagation hop,the beacon nodes whose propagation hop are fewer are retained. Then,a beacon node optimization is followed to eliminate unfavourable beacon nodes which are approximately in a line to avoid positioning failure. Besides, based on the relationship between distance estimation error and signal propagation hop derived from Friis model,the hop weighted localization algorithm is obtained. It uses the signal propagation hop as the weighted factors to modify the positioning result that gained through maximum likelihood estimation, which reduces the effect on positioning accuracy brought by distance estimation error. The simulation results show that the proposed method can improve the positioning accuracy by 3% ~5%.%针对DV-Hop定位算法中距离估计误差对定位结果的影响,提出了一种信标节点优选方案和跳数加权DV-Hop定位算法.首先通过设定跳数阈值,保留跳数较少的信标节点,然后剔除近似在一条直线上的信标节点,完成信标节点优选,避免未知节点无法定位的情形.此外,利用Friis模型推导出距离估计误差与信号传播跳数之间的映射关系,采用传播跳数作为加权因子对定位结果进行了修正.仿真结果表明该算法降低了距离估计误差对定位精度的影响,提高了定位精度.
Kauhl, Boris; Heil, Jeanne; Hoebe, Christian J. P. A.; Schweikart, Jürgen; Krafft, Thomas; Dukers-Muijrers, Nicole H. T. M.
2017-01-01
Background Despite high vaccination coverage, pertussis incidence in the Netherlands is amongst the highest in Europe with a shifting tendency towards adults and elderly. Early detection of outbreaks and preventive actions are necessary to prevent severe complications in infants. Efficient pertussis control requires additional background knowledge about the determinants of testing and possible determinants of the current pertussis incidence. Therefore, the aim of our study is to examine the possibility of locating possible pertussis outbreaks using space-time cluster detection and to examine the determinants of pertussis testing and incidence using geographically weighted regression models. Methods We analysed laboratory registry data including all geocoded pertussis tests in the southern area of the Netherlands between 2007 and 2013. Socio-demographic and infrastructure-related population data were matched to the geo-coded laboratory data. The spatial scan statistic was applied to detect spatial and space-time clusters of testing, incidence and test-positivity. Geographically weighted Poisson regression (GWPR) models were then constructed to model the associations between the age-specific rates of testing and incidence and possible population-based determinants. Results Space-time clusters for pertussis incidence overlapped with space-time clusters for testing, reflecting a strong relationship between testing and incidence, irrespective of the examined age group. Testing for pertussis itself was overall associated with lower socio-economic status, multi-person-households, proximity to primary school and availability of healthcare. The current incidence in contradiction is mainly determined by testing and is not associated with a lower socioeconomic status. Discussion Testing for pertussis follows to an extent the general healthcare seeking behaviour for common respiratory infections, whereas the current pertussis incidence is largely the result of testing. More
Chanat, Jeffrey G.; Moyer, Douglas L.; Blomquist, Joel D.; Hyer, Kenneth E.; Langland, Michael J.
2016-01-13
In the Chesapeake Bay watershed, estimated fluxes of nutrients and sediment from the bay’s nontidal tributaries into the estuary are the foundation of decision making to meet reductions prescribed by the Chesapeake Bay Total Maximum Daily Load (TMDL) and are often the basis for refining scientific understanding of the watershed-scale processes that influence the delivery of these constituents to the bay. Two regression-based flux and trend estimation models, ESTIMATOR and Weighted Regressions on Time, Discharge, and Season (WRTDS), were compared using data from 80 watersheds in the Chesapeake Bay Nontidal Water-Quality Monitoring Network (CBNTN). The watersheds range in size from 62 to 70,189 square kilometers and record lengths range from 6 to 28 years. ESTIMATOR is a constant-parameter model that estimates trends only in concentration; WRTDS uses variable parameters estimated with weighted regression, and estimates trends in both concentration and flux. WRTDS had greater explanatory power than ESTIMATOR, with the greatest degree of improvement evident for records longer than 25 years (30 stations; improvement in median model R2= 0.06 for total nitrogen, 0.08 for total phosphorus, and 0.05 for sediment) and the least degree of improvement for records of less than 10 years, for which the two models performed nearly equally. Flux bias statistics were comparable or lower (more favorable) for WRTDS for any record length; for 30 stations with records longer than 25 years, the greatest degree of improvement was evident for sediment (decrease of 0.17 in median statistic) and total phosphorus (decrease of 0.05). The overall between-station pattern in concentration trend direction and magnitude for all constituents was roughly similar for both models. A detailed case study revealed that trends in concentration estimated by WRTDS can operationally be viewed as a less-constrained equivalent to trends in concentration estimated by ESTIMATOR. Estimates of annual mean flow
Sakai, Yuki; Watanabe, Toshihiro; Wasaki, Jun; Senoura, Takeshi; Shinano, Takuro; Osaki, Mitsuru
2010-12-01
The roles of low-molecular-weight thiols (LMWTs), such as glutathione and phytochelatins, in arsenic (As) tolerance and hyperaccumulation in Pteris vittata an As-hyperaccumulator fern remain to be better understood. This study aimed to thoroughly characterize LMWT synthesis in P. vittata to understand the roles played by LMWTs in As tolerance and hyperaccumulation. LMWT synthesis in P. vittata was induced directly by As, and not by As-mediated oxidative stress. Expression of PvECS2, one of the putative genes of γ-glutamylcysteine synthetase (γECS), increases in P. vittata shoots at 48h after the onset of As exposure, almost corresponding to the increase in the concentrations of γ-glutamylcysteine and glutathione. Furthermore, localization of As showed similar trends to those of LMWTs in fronds at both whole-frond and cellular levels. This study thus indicates the specific contribution of LMWTs to As tolerance in P. vittata. γECS may be responsible for the As-induced enhancement of LMWT synthesis.
Energy Technology Data Exchange (ETDEWEB)
Cheikh, Alexandre Ben; Girouin, Nicolas [Hopital Edouard Herriot, Hospices Civils de Lyon, Department of Urinary and Vascular Radiology, Lyon (France)]|[Universite de Lyon, Lyon (France)]|[Universite de Lyon 1, faculte de medecine Lyon Nord, Lyon (France); Colombel, Marc; Marechal, Jean-Marie [Hopital Edouard Herriot, Hospices Civils de Lyon, Department of Urology, Lyon (France); Gelet, Albert [Hopital Edouard Herriot, Hospices Civils de Lyon, Department of Urology, Lyon (France)]|[Inserm, U556, Lyon (France); Bissery, Alvine; Rabilloud, Muriel [Hospices Civils de Lyon, Department of Biostatistics, Lyon (France)]|[Universite de Lyon 1, UMR CNRS 5558, Laboratoire Biostatistiques-Sante, Pierre-Benite (France); Lyonnet, Denis [Hopital Edouard Herriot, Hospices Civils de Lyon, Department of Urinary and Vascular Radiology, Lyon (France)]|[Universite de Lyon, Lyon (France)]|[Universite de Lyon 1, faculte de medecine Lyon Nord, Lyon (France)]|[Inserm, U556, Lyon (France); Rouviere, Olivier [Hopital Edouard Herriot, Hospices Civils de Lyon, Department of Urinary and Vascular Radiology, Lyon (France)]|[Universite de Lyon, Lyon (France)]|[Universite de Lyon 1, faculte de medecine Lyon Nord, Lyon (France)]|[Inserm, U556, Lyon (France)]|[Hopital Edouard Herriot, Department of Urinary and Vascular Radiology, Pavillon P Radio, Lyon Cedex 03 (France)
2009-03-15
We assessed the accuracy of T2-weighted (T2w) and dynamic contrast-enhanced (DCE) 1.5-T magnetic resonance imaging (MRI) in localizing prostate cancer before transrectal ultrasound-guided repeat biopsy. Ninety-three patients with abnormal PSA level and negative prostate biopsy underwent T2w and DCE prostate MRI using pelvic coil before repeat biopsy. T2w and DCE images were interpreted using visual criteria only. MR results were correlated with repeat biopsy findings in ten prostate sectors. Repeat biopsy found prostate cancer in 23 patients (24.7%) and 44 sectors (6.6%). At per patient analysis, the sensitivity, specificity, positive and negative predictive values were 47.8%, 44.3%, 20.4% and 79.5% for T2w imaging and 82.6%, 20%, 24.4% and 93.3% for DCE imaging. When all suspicious areas (on T2w or DCE imaging) were taken into account, a sensitivity of 82.6% and a negative predictive value of 100% could be achieved. At per sector analysis, DCE imaging was significantly less specific (83.5% vs. 89.7%, p < 0.002) than T2w imaging; it was more sensitive (52.4% vs. 32.1%), but the difference was hardly significant (p = 0.09). T2w and DCE MRI using pelvic coil and visual diagnostic criteria can guide prostate repeat biopsy, with a good sensitivity and NPV. (orig.)
Verma, Gaurav; Chawla, Sanjeev; Nagarajan, Rajakumar; Iqbal, Zohaib; Albert Thomas, M.; Poptani, Harish
2017-04-01
Two-dimensional localized correlated spectroscopy (2D L-COSY) offers greater spectral dispersion than conventional one-dimensional (1D) MRS techniques, yet long acquisition times and limited post-processing support have slowed its clinical adoption. Improving acquisition efficiency and developing versatile post-processing techniques can bolster the clinical viability of 2D MRS. The purpose of this study was to implement a non-uniformly weighted sampling (NUWS) scheme for faster acquisition of 2D-MRS. A NUWS 2D L-COSY sequence was developed for 7T whole-body MRI. A phantom containing metabolites commonly observed in the brain at physiological concentrations was scanned ten times with both the NUWS scheme of 12:48 duration and a 17:04 constant eight-average sequence using a 32-channel head coil. 2D L-COSY spectra were also acquired from the occipital lobe of four healthy volunteers using both the proposed NUWS and the conventional uniformly-averaged L-COSY sequence. The NUWS 2D L-COSY sequence facilitated 25% shorter acquisition time while maintaining comparable SNR in humans (+0.3%) and phantom studies (+6.0%) compared to uniform averaging. NUWS schemes successfully demonstrated improved efficiency of L-COSY, by facilitating a reduction in scan time without affecting signal quality.
Directory of Open Access Journals (Sweden)
Luciele Cristina Pelicioni
2009-01-01
Full Text Available Um total de 19.770 pesos corporais de bovinos Guzerá, do nascimento aos 365 dias de idade, pertencentes ao banco de dados da Associação Brasileira dos Criadores de Zebu (ABCZ foi analisado com os objetivos de comparar diferentes estruturas de variâncias residuais, considerando 1, 18, 28 e 53 classes residuais e funções de variância de ordens quadrática a quíntica; e estimar funções de co-variância de diferentes ordens para os efeitos genético aditivo direto, genético materno, de ambiente permanente de animal e de mãe e parâmetros genéticos para os pesos corporais usando modelos de regressão aleatória. Os efeitos aleatórios foram modelados por regressões polinomiais em escala de Legendre com ordens variando de linear a quártica. Os modelos foram comparados pelo teste de razão de verossimilhança e pelos critérios de Informação de Akaike e de Informação Bayesiano de Schwarz. O modelo com 18 classes heterogêneas foi o que melhor se ajustou às variâncias residuais, de acordo com os testes estatísticos, porém, o modelo com função de variância de quinta ordem também mostrou-se apropriado. Os valores de herdabilidade direta estimados foram maiores que os encontrados na literatura, variando de 0,04 a 0,53, mas seguiram a mesma tendência dos estimados pelas análises unicaracterísticas. A seleção para peso em qualquer idade melhoraria o peso em todas as idades no intervalo estudado.A total of 19,770 body weight records of Guzera cattle, measured from birth to 365 days of age and supplied by the Brazilian Zebu Breeders Association, was analyzed with the following objectives of: 1 to compare different residual variances through step functions with 1, 18, 28 and 53 classes and through variance functions with orders ranging from two to five using ordinary polynomials and 2 to estimate covariance functions considering different orders for direct additive genetic effects, animal permanent environmental and maternal
Some Simple Computational Formulas for Multiple Regression
Aiken, Lewis R., Jr.
1974-01-01
Short-cut formulas are presented for direct computation of the beta weights, the standard errors of the beta weights, and the multiple correlation coefficient for multiple regression problems involving three independent variables and one dependent variable. (Author)
Energy Technology Data Exchange (ETDEWEB)
Pekney, Natalie J.; Cheng, Hanqi; Small, Mitchell J.
2015-11-05
Abstract: The objective of the current work was to develop a statistical method and associated tool to evaluate the impact of oil and natural gas exploration and production activities on local air quality.
Institute of Scientific and Technical Information of China (English)
Christiane Jakob; Torsten Liersch; Wolfdietrich Meyer; Heinz Becker; Gustavo B Bare; Daniela E Aust
2008-01-01
AIM:To investigate the predictive value of Ki67 and p53 and their correlation with thymidylate synthase(TS) gene expression in a rectal cancer patient cohort treated according to a standardized recommended neoadjuvant treatment regimen.METHODS:Formalin fixed,paraffin embedded pretherapeutical tumor biopsies (n=22) and posttherapeutical resection specimens(n=40)from patients with rectal adenocarcinoma (clinical UICC stage Ⅱ/Ⅲ)receiving standardized neoadjuvant 5-fiuorouracil(5-FU)based chemoradiotherapy were studied for Ki67 and p53 expression by immunohistochemistry and correlated with TS mRNA expression by quantitative TaqMan realtime PCR after laser microdissection.The results were compared with histopathological tumor regression according to a standardized semiquantitative score grading system.RESULTS:Responders(patients with high tumor regression)showed a significantly lower Ki67 expression than non-responders in the pre-therapeutical tumor biopsies (81.2% vs16.7%;P＜0.05) as well as in the post-therapeutical resection specimens (75.8%vs14.3%;P＜0.01).High TS mRNA expression was significantly correlated with a high Ki67 index and low TS mRNA expression was significantly correlated with a low Ki67 index in the pre-therapeutical tumor biopsies (corr.coef.=0.46;P＜0.01)as well as in the posttherapeutical resection specimens (corr.coef.=0.40;P＜0.05).No significant association was found between p53 and TS mRNA expression or tumor regression.CONCLUSION:Ki67 has,like TS,predictive value in rectal cancer patients after neoadjuvant 5-FU based chemoradiotherapy.The close correlation between Ki67 and TS indicates that TS is involved in active cell cycle processes.
Directory of Open Access Journals (Sweden)
Athanasios eTsanas
2015-04-01
Full Text Available Sleep spindles are critical in characterizing sleep and have been associated with cognitive function and pathophysiological assessment. Typically, their detection relies on the subjective and time-consuming visual examination of electroencephalogram (EEG signal(s by experts, and has led to large inter-rater variability as a result of poor definition of sleep spindle characteristics. Hitherto, many algorithmic spindle detectors inherently make signal stationarity assumptions (e.g. Fourier transform-based approaches which are inappropriate for EEG signals, and frequently rely on additional information which may not be readily available in many practical settings (e.g. more than one EEG channels, or prior hypnogram assessment. This study proposes a novel signal processing methodology relying solely on a single EEG channel, and provides objective, accurate means towards probabilistically assessing the presence of sleep spindles in EEG signals. We use the intuitively appealing continuous wavelet transform (CWT with a Morlet basis function, identifying regions of interest where the power of the CWT coefficients corresponding to the frequencies of spindles (11-16 Hz is large. The potential for assessing the signal segment as a spindle is refined using local weighted smoothing techniques. We evaluate our findings on two databases: the MASS database comprising 19 healthy controls and the DREAMS sleep spindle database comprising eight participants diagnosed with various sleep pathologies. We demonstrate that we can replicate the experts’ sleep spindles assessment accurately in both databases (MASS database: sensitivity: 84%, specificity: 90%, false discovery rate 83%, DREAMS database: sensitivity: 76%, specificity: 92%, false discovery rate: 67%, outperforming six competing automatic sleep spindle detection algorithms in terms of correctly replicating the experts’ assessment of detected spindles.
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.
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.…
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.…
Institute of Scientific and Technical Information of China (English)
王启军; 赵虎; 张红星; 吉红
2012-01-01
Giant salamanders {Andrias davidianus), which is Amphibia, Caudata, salamander Cryptobranchus Branch, Megalobatrachus, is a unique endemic amphibious in China. Recently, breeding giant salamanders in historical distribution areas is a hot spot, however, the assessment methods of breeding effects are still rare. This essay analyzed the whole length as well as the weight of 1 530 giant salamanders using regression analysis in SPSS analysis software. The results indicated that there was a significant correlative relation between the weight and the whole length of giant salamanders. The formula was Y=0.010X2.867. In conclusion, this article provided a scientific method to assess the status of giant salamander via artificial breeding.%大鲵(Andrias davidianus)隶属两栖纲,有尾目,隐鳃鲵科,大鲵属,为我国特有的珍稀濒危两栖动物.目前在我国一些主要历史分布区掀起了一股大鲵养殖高潮,但是对当前养殖效果缺乏成熟的评价手段,因此本研究通过对陕西省汉中市和安康市两大鲵养殖场养殖的1 530尾大鲵的体重和全长进行实际测量,利用SPSS分析软件对数据进行了回归分析.结果表明,体重与全长之间存在极显著的相关关系,体重与全长之间的关系主要表现为幂函数关系,其关系式为:Y=0.010X2.867.本研究的顺利完成,为今后评价大鲵的人工养殖效果提供了科学的方法.
Directory of Open Access Journals (Sweden)
Abílio Amiguinho
2005-01-01
Full Text Available The process of socio-educational territorialisation in rural contexts is the topic of this text. The theme corresponds to a challenge to address it having as main axis of discussion either the problem of social exclusion or that of local development. The reasons to locate the discussion in this last field of analysis are discussed in the first part of the text. Theoretical and political reasons are there articulated because the question is about projects whose intentions and practices call for the political both in the theoretical debate and in the choices that anticipate intervention. From research conducted for several years, I use contributions that aim at discuss and enlighten how school can be a potential locus of local development. Its identification and recognition as local institution (either because of those that work and live in it or because of those that act in the surrounding context are crucial steps to progressively constitute school as a partner for development. The promotion of the local values and roots, the reconstruction of socio-personal and local identities, the production of sociabilities and the equation and solution of shared problems were the dimensions of a socio-educative intervention, markedly globalising. This scenario, as it is argued, was also, intentionally, one of transformation and of deliberate change of school and of the administration of the educative territoires.
Directory of Open Access Journals (Sweden)
P. Tholon
2011-04-01
Full Text Available The objective of this work was to determine genetic parameters for body weight of tinamou in captivity. It was used random regression models in analyses of data by considering the direct additive genetic (DA and permanent environmental effects of the animal (PE as random effects. Residual variances were modeled by using a fifth-order variance function. The mean population growth curve was fitted by sixth-order Legendre orthogonal polynomials. Direct additive genetic effects and animal environmental permanent effect were modeled by using Legendre polynomials of order two to nine. The best results were obtained by models with orders of fit of 6 for direct additive genetic effect and of order 3 for permanent effect by Akaike information criterion and of order 3 for both additive genetic effect and permanent effect by Schwarz Bayesian information criterion and likelihood ratio test. Heritability estimates ranged from 0.02 to 0.57. The first eigenvalue explained 94% and 90% of the variation from additive direct and permant environmental effects, respectively. Selection of tinamou for body weight is more effective after 112 days of age.Com este trabalho objetivou-se determinar parâmetros genéticos para peso corporal de perdizes em cativeiro. Foram utilizados modelos de regressão aleatória na análise dos dados considerando os efeitos genéticos aditivos diretos (AD e de ambiente permanente de animal (AP como aleatórios. As variâncias residuais foram modeladas utilizando-se funções de variância de ordem 5. A curva média da população foi ajustada por polinômios ortogonais de Legendre de ordem 6. Os efeitos genéticos aditivos diretos e de ambiente permanente de animal foram modelados utilizando-se polinômios de Legendre de segunda a nona ordem. Os melhores resultados foram obtidos pelos modelos de ordem 6 de ajuste para os efeitos genéticos aditivos diretos e de ordem 3 para os de ambiente permanente pelo Critério de Informação de
Nurfeta, Ajebu
2010-06-01
Effects of supplementing sheep consuming wheat straw with local agro-industrial by-products on feed intake, growth, digestibility and nitrogen utilization were determined. Thirty 1-year-old local wethers, with a mean (+/-SD) live weight of 19.8 (+/-1.06) kg, were assigned to five treatments: wheat straw + atella (T1), wheat straw + atella + poultry litter (T2), wheat straw + atella + coffee pulp (T3), wheat straw + atella + coffee pulp + poultry litter (T4), hay + concentrate (T5). A 7-day digestibility experiment and a 112-day growth trial were conducted. Total dry matter (DM) and organic matter (OM) intake as well as body weight gain was similar for all treatments. The highest (P coffee pulp are available, smallholder farmers could feed the mixtures as a supplement to straw with a good performance without using concentrate feeds.
Jong WH de; Beek M ter; Veenman C; Klerk A de; Loveren H van; TOX
2006-01-01
The results of the local lymph node assay are not for all compounds useful as starting point for a quantitative risk assessment. This study describes the effects after repeated exposure of the skin to a concentration of a sensitizer below the threshold used in the local lymph node assay. Positive re
Interior Lp-estimates for elliptic and parabolic Schr\\"odinger type operators and local Ap-weights
Cardoso, Isolda; Viola, Pablo; Viviani, Beatriz
2015-01-01
Let Omega be a non-empty open proper and connected subset of R^n. Consider p elliptic Schr\\"odinger type operator L_{E}u=A_{E}u+V in Omega, and the linear parabolic operator L_{P}u=A_{P}u+Vu in Omega x (0,T), where the coefficients of A_{E} and A_{P} are in VMO and the potential V satisfies a reverse-H\\"older condition. The aim of this paper is to obtain a priori estimates for the operators L_{E} and L_{P} in weighted Sobolev spaces involving the distance to the boundary and weights in a loca...
Directory of Open Access Journals (Sweden)
Angelita Puji Lestari
2015-04-01
Full Text Available Plant breeding program consists of establishment of the population, selection, and evaluation. The study aimed to observe the variability of yield components, the heritability, and the distribution of the yield component characters in the F3 populations. The experiment was conducted at Muara Experimental Farm Bogor, from April to August 2012 on Latosol soil. The F3 populations derived from crosses of Bintang Ladang x US2, Gampai x IR77674, and Progol x Asahan and their parental were used as plant materials. Twenty one-day-old seedlings from each population were planted in plots of 2 x 12 m, with planting space of 20 x 20 cm and 3-5 seedlings per hole. Panicle length and weight were observed on 300 randomly selected plant samples from each population. The results showed that there was a variation of agronomic characters among genotypes. The heritability of characters, the panicle length and weight was low to high. Panicle length and weight were controlled by many genes with additive gene action in the Gampai x IR77674 derived population, while panicle weight was controlled by few genes with complementary epistatic additive gene action in both Bintang Ladang x US2 and Progol x Asahan derived populations. The more genes controlling a character, the more distribution classes formed and the greater variance among genotypes.
Kauhl, Boris; Schweikart, Jürgen; Krafft, Thomas; Keste, Andrea; Moskwyn, Marita
2016-11-03
The provision of general practitioners (GPs) in Germany still relies mainly on the ratio of inhabitants to GPs at relatively large scales and barely accounts for an increased prevalence of chronic diseases among the elderly and socially underprivileged populations. Type 2 Diabetes Mellitus (T2DM) is one of the major cost-intensive diseases with high rates of potentially preventable complications. Provision of healthcare and access to preventive measures is necessary to reduce the burden of T2DM. However, current studies on the spatial variation of T2DM in Germany are mostly based on survey data, which do not only underestimate the true prevalence of T2DM, but are also only available on large spatial scales. The aim of this study is therefore to analyse the spatial distribution of T2DM at fine geographic scales and to assess location-specific risk factors based on data of the AOK health insurance. To display the spatial heterogeneity of T2DM, a bivariate, adaptive kernel density estimation (KDE) was applied. The spatial scan statistic (SaTScan) was used to detect areas of high risk. Global and local spatial regression models were then constructed to analyze socio-demographic risk factors of T2DM. T2DM is especially concentrated in rural areas surrounding Berlin. The risk factors for T2DM consist of proportions of 65-79 year olds, 80 + year olds, unemployment rate among the 55-65 year olds, proportion of employees covered by mandatory social security insurance, mean income tax, and proportion of non-married couples. However, the strength of the association between T2DM and the examined socio-demographic variables displayed strong regional variations. The prevalence of T2DM varies at the very local level. Analyzing point data on T2DM of northeastern Germany's largest health insurance provider thus allows very detailed, location-specific knowledge about increased medical needs. Risk factors associated with T2DM depend largely on the place of residence of the
Institute of Scientific and Technical Information of China (English)
王丽荣
2014-01-01
For the robust face recognition problem with high-dimensional small sample, the algorithm of spectral regression classification optimized by local linear embedding is proposed. Firstly, feature vectors of training samples are calculated. Then, local linear embedding is used to construct embedding needed by classification and embeddings needed by sub-manifold of each classification is learned. Finally, spectral regression classification algorithm is used to compute project metrics, and nearest neighbor classifier is used to recognize face. Experimental results on the common face datasets FERET, AR and Extended YaleB show that proposed algorithm has better recognition efficiency than several other spectral regression algorithms.%针对高维小样本鲁棒人脸识别问题，提出了一种局部线性嵌入优化光谱回归算法。计算出训练样本的特征向量，然后用局部线性嵌入算法构建分类问题所需的嵌入，并学习每种分类的子流形所需的嵌入；利用光谱回归计算投影矩阵，最近邻分类器完成人脸的识别。在人脸数据库FERET、AR及扩展YaleB上的实验结果表明，相比其他几种光谱回归算法，该算法取得了更好的识别效果。
基于最大似然估计的加权质心定位算法%Weighted Centroid Localization Algorithm Based on Maximum Likelihood Estimation
Institute of Scientific and Technical Information of China (English)
卢先领; 夏文瑞
2016-01-01
In solving the problem of localizing nodes in a wireless sensor network,we propose a weighted centroid localization algorithm based on maximum likelihood estimation,with the specific goal of solving the problems of big received signal strength indication (RSSI)ranging error and low accuracy of the centroid localization algorithm.Firstly,the maximum likelihood estimation between the estimated distance and the actual distance is calculated as weights.Then,a parameter k is introduced to optimize the weights between the anchor nodes and the unknown nodes in the weight model.Finally,the locations of the unknown nodes are calculated and modified by using the proposed algorithm.The simulation results show that the weighted centroid algorithm based on the maximum likelihood estimation has the features of high localization accuracy and low cost,and has better performance compared with the inverse distance-based algorithm and the inverse RSSI-based algo-rithm.Hence,the proposed algorithm is more suitable for the indoor localization of large areas.%为解决无线传感器网络中节点自身定位问题，针对接收信号强度指示（received signal strength indication，RSSI）测距误差大和质心定位算法精度低的问题，提出一种基于最大似然估计的加权质心定位算法。首先通过计算将估计距离与实际距离之间的最大似然估计值作为权值，然后在权值模型中，引进一个参数k优化未知节点周围锚节点分布，最后计算出未知节点的位置并加以修正。仿真结果表明，基于最大似然估计的加权质心算法具有定位精度高和成本低的特点，优于基于距离倒数的质心加权和基于RSSI倒数的质心加权算法，适用于大面积的室内定位。
Regression analysis by example
National Research Council Canada - National Science Library
Chatterjee, Samprit; Hadi, Ali S
2012-01-01
.... The emphasis continues to be on exploratory data analysis rather than statistical theory. The coverage offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression...
Berger, Nicole; Varga, Zsuzsanna; Frauenfelder, Thomas; Boss, Andreas
2017-05-01
In magnetic resonance-guided breast vacuum biopsies, the contrast agent for targeting suspicious lesions can typically be applied only once during an intervention, due to the slow elimination of the gadolinium chelate from the extracellular fluid space. This study evaluated the feasibility of diffusion-weighted imaging (DWI) for lesion targeting in vacuum assisted magnetic resonance imaging (MRI) biopsies. DWI may be used as an alternative to dynamic contrast-enhanced MRI with the advantage of reproducibility. However, the targeted lesion requires the characteristics of a mass-like lesion, substantial diffusion restriction, and a minimum size of approximately 1cm. Copyright © 2016 Elsevier Inc. All rights reserved.
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 e...
Energy Technology Data Exchange (ETDEWEB)
Gillmann, Clarissa, E-mail: clarissa.gillmann@med.uni-heidelberg.de [Department of Radiation Oncology and Radiation Therapy, Heidelberg University Hospital, Heidelberg (Germany); Jäkel, Oliver [Department of Radiation Oncology and Radiation Therapy, Heidelberg University Hospital, Heidelberg (Germany); Heidelberg Ion Beam Therapy Center (HIT), Heidelberg (Germany); Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg (Germany); Schlampp, Ingmar [Department of Radiation Oncology and Radiation Therapy, Heidelberg University Hospital, Heidelberg (Germany); Karger, Christian P. [Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg (Germany)
2014-04-01
Purpose: To compare the relative biological effectiveness (RBE)–weighted tolerance doses for temporal lobe reactions after carbon ion radiation therapy using 2 different versions of the local effect model (LEM I vs LEM IV) for the same patient collective under identical conditions. Methods and Materials: In a previous study, 59 patients were investigated, of whom 10 experienced temporal lobe reactions (TLR) after carbon ion radiation therapy for low-grade skull-base chordoma and chondrosarcoma at Helmholtzzentrum für Schwerionenforschung (GSI) in Darmstadt, Germany in 2002 and 2003. TLR were detected as visible contrast enhancements on T1-weighted MRI images within a median follow-up time of 2.5 years. Although the derived RBE-weighted temporal lobe doses were based on the clinically applied LEM I, we have now recalculated the RBE-weighted dose distributions using LEM IV and derived dose-response curves with Dmax,V-1 cm³ (the RBE-weighted maximum dose in the remaining temporal lobe volume, excluding the volume of 1 cm³ with the highest dose) as an independent dosimetric variable. The resulting RBE-weighted tolerance doses were compared with those of the previous study to assess the clinical impact of LEM IV relative to LEM I. Results: The dose-response curve of LEM IV is shifted toward higher values compared to that of LEM I. The RBE-weighted tolerance dose for a 5% complication probability (TD{sub 5}) increases from 68.8 ± 3.3 to 78.3 ± 4.3 Gy (RBE) for LEM IV as compared to LEM I. Conclusions: LEM IV predicts a clinically significant increase of the RBE-weighted tolerance doses for the temporal lobe as compared to the currently applied LEM I. The limited available photon data do not allow a final conclusion as to whether RBE predictions of LEM I or LEM IV better fit better clinical experience in photon therapy. The decision about a future clinical application of LEM IV therefore requires additional analysis of temporal lobe reactions in a
Wu, Chunhung
2016-04-01
Few researches have discussed about the applicability of applying the statistical landslide susceptibility (LS) model for extreme rainfall-induced landslide events. The researches focuses on the comparison and applicability of LS models based on four methods, including landslide ratio-based logistic regression (LRBLR), frequency ratio (FR), weight of evidence (WOE), and instability index (II) methods, in an extreme rainfall-induced landslide cases. The landslide inventory in the Chishan river watershed, Southwestern Taiwan, after 2009 Typhoon Morakot is the main materials in this research. The Chishan river watershed is a tributary watershed of Kaoping river watershed, which is a landslide- and erosion-prone watershed with the annual average suspended load of 3.6×107 MT/yr (ranks 11th in the world). Typhoon Morakot struck Southern Taiwan from Aug. 6-10 in 2009 and dumped nearly 2,000 mm of rainfall in the Chishan river watershed. The 24-hour, 48-hour, and 72-hours accumulated rainfall in the Chishan river watershed exceeded the 200-year return period accumulated rainfall. 2,389 landslide polygons in the Chishan river watershed were extracted from SPOT 5 images after 2009 Typhoon Morakot. The total landslide area is around 33.5 km2, equals to the landslide ratio of 4.1%. The main landslide types based on Varnes' (1978) classification are rotational and translational slides. The two characteristics of extreme rainfall-induced landslide event are dense landslide distribution and large occupation of downslope landslide areas owing to headward erosion and bank erosion in the flooding processes. The area of downslope landslide in the Chishan river watershed after 2009 Typhoon Morakot is 3.2 times higher than that of upslope landslide areas. The prediction accuracy of LS models based on LRBLR, FR, WOE, and II methods have been proven over 70%. The model performance and applicability of four models in a landslide-prone watershed with dense distribution of rainfall
Cao, Jun-Zhe; Liu, Wen-Qi; Gu, Hong
2012-11-01
Machine learning is a kind of reliable technology for automated subcellular localization of viral proteins within a host cell or virus-infected cell. One challenge is that the viral protein samples are not only with multiple location sites, but also class-imbalanced. The imbalanced dataset often decreases the prediction performance. In order to accomplish this challenge, this paper proposes a novel approach named imbalance-weighted multi-label K-nearest neighbor to predict viral protein subcellular location with multiple sites. The experimental results by jackknife test indicate that the presented algorithm achieves a better performance than the existing methods and has great potentials in protein science.
Dean, D D; Schwartz, Z; Blanchard, C R; Liu, Y; Agrawal, C M; Lohmann, C H; Sylvia, V L; Boyan, B D
1999-01-01
Small particles of ultrahigh molecular weight polyethylene stimulate formation of foreign-body granulomas and bone resorption. Bone formation may also be affected by wear debris. To determine if wear debris directly affects osteoblasts, we characterized a commercial preparation of ultrahigh molecular weight polyethylene (GUR4150) particles and examined their effect on MG63 osteoblast-like cells. In aliquots of the culture medium containing ultrahigh molecular weight polyethylene, 79% of the particles were less than 1 microm in diameter, indicating that the cells were exposed to particles of less than 1 microm. MG63 cell response to the particles was measured by assaying cell number, [3H]thymidine incorporation, alkaline phosphatase specific activity, osteocalcin production, [35S]sulfate incorporation, and production of prostaglandin E2 and transforming growth factor-beta. Cell number and [3H]thymidine incorporation were increased in a dose-dependent manner. Alkaline phosphatase specific activity, a marker of cell differentiation for the cultures, was significantly decreased, but osteocalcin production was not affected. [35S]sulfate incorporation, a measure of extracellular matrix production, was reduced. Prostaglandin E2 release was increased, but transforming growth factor-beta production was decreased in a dose-dependent manner. This shows that ultrahigh molecular weight polyethylene particles affect MG63 proliferation, differentiation, extracellular matrix synthesis, and local factor production. These effects were direct and dose dependent. The findings suggest that ultrahigh molecular weight polyethylene wear debris particles with an average size of approximately 1 microm may inhibit bone formation by inhibiting cell differentiation and reducing transforming growth factor-beta production and matrix synthesis. In addition, increases in prostaglandin E2 production may not only affect osteoblasts by an autocrine pathway but may also stimulate the proliferation and
Stahel-Donoho kernel estimation for fixed design nonparametric regression models
Institute of Scientific and Technical Information of China (English)
LIN; Lu
2006-01-01
This paper reports a robust kernel estimation for fixed design nonparametric regression models.A Stahel-Donoho kernel estimation is introduced,in which the weight functions depend on both the depths of data and the distances between the design points and the estimation points.Based on a local approximation,a computational technique is given to approximate to the incomputable depths of the errors.As a result the new estimator is computationally efficient.The proposed estimator attains a high breakdown point and has perfect asymptotic behaviors such as the asymptotic normality and convergence in the mean squared error.Unlike the depth-weighted estimator for parametric regression models,this depth-weighted nonparametric estimator has a simple variance structure and then we can compare its efficiency with the original one.Some simulations show that the new method can smooth the regression estimation and achieve some desirable balances between robustness and efficiency.
DEFF Research Database (Denmark)
Haack, Søren; Tanderup, Kari; Kallehauge, Jesper Folsted
2015-01-01
distribution of ADC values. This study evaluates: 1) different segmentation methods; and 2) how they affect assessment of tumor ADC value during RT. MATERIAL AND METHODS: Eleven patients with locally advanced cervical cancer underwent MRI three times during their RT: prior to start of RT (PRERT), two weeks.......01), and the volumes changed significantly during treatment (p ....52 ± 0.3). There was no significant difference in mean ADC value compared at same treatment time. Mean tumor ADC value increased significantly (p treatment time. CONCLUSION: Among the three semi-automatic segmentations of hyper-intense intensities on DW-MR images...
一种改进的局部加权拟合校正方法%An Improved Locally-weighted-fitting Technique
Institute of Scientific and Technical Information of China (English)
王生明; 李永树; 何敬
2013-01-01
几何校正是遥感图像处理的一个重要环节,是减弱遥感图像与地面真实形态差异的重要方法.但是常规几何校正方法对无人机影像进行校正,其效果不够理想.在分析了局部加权拟合校正法的基础上,用控制圆规定其局部校正区域,并且新增辅助控制点来覆盖该区域,以这种方法对其进行了改进.通过对同一无人机影像进行几何校正对比分析实验,结果表明:改进的局部加权拟合校正法能够更充分、合理地利用局部控制点信息,对控制点的分布具有更好的适应性,能够有效提高无人机影像的几何校正精度.%In remote sensing image process,geometric correction is an important component that can reduce or eliminate differences between remote sensing image and real geographic shape. But the effect of conventional technique for UAV image geometric correction is not satisfactory, it improves the locally-weighted-fitting technique by using control circle to regulate its local corrected region and adding auxiliary control point to the cover area. By contrast geometric correction analysis experiment, it shows that the locally-weighted-fitting technique improved may be more adequate and reasonable use of local information, has stronger adaptability for the distribution of control points, and can effectively improve the accuracy of the image geometric correction.
Weighted Improved Euclidean Localization Algorithm Based on ZigBee%基于ZigBee的加权改进Euclidean定位算法
Institute of Scientific and Technical Information of China (English)
金纯; 何山; 胡建农; 周亮; 徐洪刚
2011-01-01
RSSI-based triangle and centroid location sometimes can not work, in order to solve this problem, weighted improved Euclidean localization algorithm is proposed based on ZigBee. The algorithm uses the least square method to derive the parameters of the environment, and then the weighted improved Euclidean localization algorithm is used to locate the unknown nodes. The result of the simulation and experiment proves that it can be applied to some applications.%针对基于RSSI的三角形质心定位算法会出现不能工作的情况,提出了基于ZigBee的加权改进Euclidean定位算法.此算法利用最小二乘法得到环境参数,然后利用加权改进Euclidean定位算法对未知节点进行定位.仿真和实验表明,该算法具有一定的实用价值.
Institute of Scientific and Technical Information of China (English)
JIANG Jingfei; ZHANG Jianqiu
2012-01-01
In this paper,the source localization by utilizing the measurements of a single electromagnetic (EM) vector-sensor is investigated in the framework of the geometric algebra of Euclidean 3-space.In order to describe the orthogonality among the electric and magnetic measurements,two multivectors of the geometric algebra of Euclidean 3-space (G3) are used to model the outputs of a spatially collocated EM vector-sensor.Two estimators for the wave propagation vector estimation are then formulated by the inner product between a vector and a bivector in the G3.Since the information used by the two estimators is different,a weighted inner product estimator is then proposed to fuse the two estimators together in the sense of the minimum mean square error (MMSE).Analytical results show that the statistical performances of the weighted inner product estimator are always better than its traditional cross product counterpart.The efficacy of the weighted inner product estimator and the correctness of the analytical predictions are demonstrated by simulation results.
Regression analysis by example
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
Unitary Response Regression Models
Lipovetsky, S.
2007-01-01
The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…
Flexible survival regression modelling
DEFF Research Database (Denmark)
Cortese, Giuliana; Scheike, Thomas H; Martinussen, Torben
2009-01-01
Regression analysis of survival data, and more generally event history data, is typically based on Cox's regression model. We here review some recent methodology, focusing on the limitations of Cox's regression model. The key limitation is that the model is not well suited to represent time-varyi...
DEFF Research Database (Denmark)
Fitzenberger, Bernd; Wilke, Ralf Andreas
2015-01-01
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 m...... treatment of the topic is based on the perspective of applied researchers using quantile regression in their empirical work....
Naghshpour, Shahdad
2012-01-01
Regression analysis is the most commonly used statistical method in the world. Although few would characterize this technique as simple, regression is in fact both simple and elegant. The complexity that many attribute to regression analysis is often a reflection of their lack of familiarity with the language of mathematics. But regression analysis can be understood even without a mastery of sophisticated mathematical concepts. This book provides the foundation and will help demystify regression analysis using examples from economics and with real data to show the applications of the method. T
Institute of Scientific and Technical Information of China (English)
付国宁; 董禹澎; 陈建仁
2012-01-01
In this paper, the basic properties of double points local Ap weights on Rd( d 〉 1 ) are studied. Especially, local Ap weights satisfy the reverse Holder inequality.%讨论R^d（d〉1）上双点局部A，权的性质．特别的证明了双点局部Ap权也满足反Holder不等式．
Institute of Scientific and Technical Information of China (English)
付锴; 雷勇; 颜嘉俊
2011-01-01
The traditional Multi-Dimensional Scaling ( MDS) algorithm adopts multi-hop distance to replace direct distance, resulting in low accuracy of the local network and large localization error in irregular network. Relative to the existing algorithms, the paper introduced the Euclidean algorithm to generate accurate multi-hop distance between nodes, and used weighting mechanism to improve the coefficient of stress. The simulation results show that in low connectivity rectangle network and C-shape network localization, this method acTheves better performance.%传统的多维定标(MDS)算法由于采用多跳距离代替节点间的直接距离,生成的局部网络准确度低,在不规则网络中定位误差大.相对于现有的算法,引入Euclidean方法来产生多跳节点间的准确距离,并采用一种加权机制来改进协强系数,以抑制累积误差.仿真结果表明该方法在C型网络和低连通度的矩形网络定位中能取得更好的效果.
Autistic epileptiform regression.
Canitano, Roberto; Zappella, Michele
2006-01-01
Autistic regression is a well known condition that occurs in one third of children with pervasive developmental disorders, who, after normal development in the first year of life, undergo a global regression during the second year that encompasses language, social skills and play. In a portion of these subjects, epileptiform abnormalities are present with or without seizures, resembling, in some respects, other epileptiform regressions of language and behaviour such as Landau-Kleffner syndrome. In these cases, for a more accurate definition of the clinical entity, the term autistic epileptifom regression has been suggested. As in other epileptic syndromes with regression, the relationships between EEG abnormalities, language and behaviour, in autism, are still unclear. We describe two cases of autistic epileptiform regression selected from a larger group of children with autistic spectrum disorders, with the aim of discussing the clinical features of the condition, the therapeutic approach and the outcome.
Scaled Sparse Linear Regression
Sun, Tingni
2011-01-01
Scaled sparse linear regression jointly estimates the regression coefficients and noise level in a linear model. It chooses an equilibrium with a sparse regression method by iteratively estimating the noise level via the mean residual squares and scaling the penalty in proportion to the estimated noise level. The iterative algorithm costs nearly nothing beyond the computation of a path of the sparse regression estimator for penalty levels above a threshold. For the scaled Lasso, the algorithm is a gradient descent in a convex minimization of a penalized joint loss function for the regression coefficients and noise level. Under mild regularity conditions, we prove that the method yields simultaneously an estimator for the noise level and an estimated coefficient vector in the Lasso path satisfying certain oracle inequalities for the estimation of the noise level, prediction, and the estimation of regression coefficients. These oracle inequalities provide sufficient conditions for the consistency and asymptotic...
Directory of Open Access Journals (Sweden)
Ashwini Keskar
2015-12-01
Full Text Available Length-weight (LWR and length-length (LLR relationships of seven loach species (Teleostei: Cypriniformes: Botia striata, Lepidocephalichthys thermalis, Paracanthocobitis mooreh, Indoreonectes evezardi, Nemacheilus anguilla, Nemachilichthys rueppelli and Schistura denisoni were studied from five localities within the Krishna River system of the Indian Western Ghats: Lonawala (Indrayani River, Paud (Mula River, Warje (Mutha River, Bhor (Nira River and Patan (Koyna River. With the exception of L. thermalis all species are endemic to peninsular India, and to our knowledge this is the first presentation of LWR and LLR data for them. New maximum lengths are also reported for I. evezardi, N. anguilla, N. rueppelli and S. denisoni.
Rolling Regressions with Stata
Kit Baum
2004-01-01
This talk will describe some work underway to add a "rolling regression" capability to Stata's suite of time series features. Although commands such as "statsby" permit analysis of non-overlapping subsamples in the time domain, they are not suited to the analysis of overlapping (e.g. "moving window") samples. Both moving-window and widening-window techniques are often used to judge the stability of time series regression relationships. We will present an implementation of a rolling regression...
Institute of Scientific and Technical Information of China (English)
Guijun YANG; Lu LIN; Runchu ZHANG
2007-01-01
Quasi-regression, motivated by the problems arising in the computer experiments, focuses mainly on speeding up evaluation. However, its theoretical properties are unexplored systemically. This paper shows that quasi-regression is unbiased, strong convergent and asymptotic normal for parameter estimations but it is biased for the fitting of curve. Furthermore, a new method called unbiased quasi-regression is proposed. In addition to retaining the above asymptotic behaviors of parameter estimations, unbiased quasi-regression is unbiased for the fitting of curve.
Introduction to regression graphics
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
Weisberg, Sanford
2005-01-01
Master linear regression techniques with a new edition of a classic text Reviews of the Second Edition: ""I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression."" -Technometrics, February 1987 ""Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis."" -American Scientist, May-June 1987
Counting people with low-level features and Bayesian regression.
Chan, Antoni B; Vasconcelos, Nuno
2012-04-01
An approach to the problem of estimating the size of inhomogeneous crowds, which are composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking is proposed. Instead, the crowd is segmented into components of homogeneous motion, using the mixture of dynamic-texture motion model. A set of holistic low-level features is extracted from each segmented region, and a function that maps features into estimates of the number of people per segment is learned with Bayesian regression. Two Bayesian regression models are examined. The first is a combination of Gaussian process regression with a compound kernel, which accounts for both the global and local trends of the count mapping but is limited by the real-valued outputs that do not match the discrete counts. We address this limitation with a second model, which is based on a Bayesian treatment of Poisson regression that introduces a prior distribution on the linear weights of the model. Since exact inference is analytically intractable, a closed-form approximation is derived that is computationally efficient and kernelizable, enabling the representation of nonlinear functions. An approximate marginal likelihood is also derived for kernel hyperparameter learning. The two regression-based crowd counting methods are evaluated on a large pedestrian data set, containing very distinct camera views, pedestrian traffic, and outliers, such as bikes or skateboarders. Experimental results show that regression-based counts are accurate regardless of the crowd size, outperforming the count estimates produced by state-of-the-art pedestrian detectors. Results on 2 h of video demonstrate the efficiency and robustness of the regression-based crowd size estimation over long periods of time.
Regression-based air temperature spatial prediction models: an example from Poland
Directory of Open Access Journals (Sweden)
Mariusz Szymanowski
2013-10-01
Full Text Available A Geographically Weighted Regression ? Kriging (GWRK algorithm, based on the local Geographically Weighted Regression (GWR, is applied for spatial prediction of air temperature in Poland. Hengl's decision tree for selecting a suitable prediction model is extended for varying spatial relationships between the air temperature and environmental predictors with an assumption of existing environmental dependence of analyzed temperature variables. The procedure includes the potential choice of a local GWR instead of the global Multiple Linear Regression (MLR method for modeling the deterministic part of spatial variation, which is usual in the standard regression (residual kriging model (MLRK. The analysis encompassed: testing for environmental correlation, selecting an appropriate regression model, testing for spatial autocorrelation of the residual component, and validating the prediction accuracy. The proposed approach was performed for 69 air temperature cases, with time aggregation ranging from daily to annual average air temperatures. The results show that, irrespective of the level of data aggregation, the spatial distribution of temperature is better fitted by local models, and hence is the reason for choosing a GWR instead of the MLR for all variables analyzed. Additionally, in most cases (78% there is spatial autocorrelation in the residuals of the deterministic part, which suggests that the GWR model should be extended by ordinary kriging of residuals to the GWRK form. The decision tree used in this paper can be considered as universal as it encompasses either spatially varying relationships of modeled and explanatory variables or random process that can be modeled by a stochastic extension of the regression model (residual kriging. Moreover, for all cases analyzed, the selection of a method based on the local regression model (GWRK or GWR does not depend on the data aggregation level, showing the potential versatility of the technique.
Energy Technology Data Exchange (ETDEWEB)
Gerber, Samuel [Univ. of Utah, Salt Lake City, UT (United States); Rubel, Oliver [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Bremer, Peer -Timo [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Pascucci, Valerio [Univ. of Utah, Salt Lake City, UT (United States); Whitaker, Ross T. [Univ. of Utah, Salt Lake City, UT (United States)
2012-01-19
This paper introduces a novel partition-based regression approach that incorporates topological information. Partition-based regression typically introduces a quality-of-fit-driven decomposition of the domain. The emphasis in this work is on a topologically meaningful segmentation. Thus, the proposed regression approach is based on a segmentation induced by a discrete approximation of the Morse–Smale complex. This yields a segmentation with partitions corresponding to regions of the function with a single minimum and maximum that are often well approximated by a linear model. This approach yields regression models that are amenable to interpretation and have good predictive capacity. Typically, regression estimates are quantified by their geometrical accuracy. For the proposed regression, an important aspect is the quality of the segmentation itself. Thus, this article introduces a new criterion that measures the topological accuracy of the estimate. The topological accuracy provides a complementary measure to the classical geometrical error measures and is very sensitive to overfitting. The Morse–Smale regression is compared to state-of-the-art approaches in terms of geometry and topology and yields comparable or improved fits in many cases. Finally, a detailed study on climate-simulation data demonstrates the application of the Morse–Smale regression. Supplementary Materials are available online and contain an implementation of the proposed approach in the R package msr, an analysis and simulations on the stability of the Morse–Smale complex approximation, and additional tables for the climate-simulation study.
DEFF Research Database (Denmark)
Bordacconi, Mats Joe; Larsen, Martin Vinæs
2014-01-01
Humans are fundamentally primed for making causal attributions based on correlations. This implies that researchers must be careful to present their results in a manner that inhibits unwarranted causal attribution. In this paper, we present the results of an experiment that suggests 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...... 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...
Institute of Scientific and Technical Information of China (English)
刘琼峰; 李明德; 段建南; 吴海勇; 洪曦
2013-01-01
Contamination of suburban, agricultural soils with heavy metals draws great attention because of its potential threat to food safety and its detrimental effects on the ecosystem. The origins of soil heavy metals in the suburban interface are usually controlled by many factors, such as parent material, industrial activities, and agriculture. To decrease heavy metals pollution risks effectively in suburban areas and further to establish reliable protection measures, it is quite necessary to understand their sources and spatial patterns. The ordinary linear regression model (OLS) has been frequently used to analyze the relationship between soil heavy metals and their influential factors. However, OLS is only in a global or an average sense to estimate parameters, and it is unable to reflect spatial local variation or test spatial non-stationarity.Geographically weighted regression models (GWR) are a powerful tool for exploring spatial heterogeneity. The underlying idea of GWR is that parameters may be estimated anywhere in the study area given a dependent variable and a set of one or more independent variables which have been measured at known locations. Not only can it test spatial non-stationarity, but it can also provide the corresponding solutions. As a local model, GWR modeling has been applied in research on urban housing land prices and the spatial factors of economic development, but it has seldom been applied to the origins and spatial structure of soil heavy metals. A survey was conducted in this study to determine the possible sources of heavy metals in agricultural soils of the suburban area of Changsha. A total of 513 surface soil samples were collected, and the concentrations of Pb and Cd were analyzed. Typical influences on soil Pb and Cd concentration were identified from soil properties and geographic locations, such as soil pH, organic matter, alkali-hydro nitrogen, rapidly available phosphorus, rapidly available potassium, slowly available potassium
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
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.
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-
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
Transductive Ordinal Regression
Seah, Chun-Wei; Ong, Yew-Soon
2011-01-01
Ordinal regression is commonly formulated as a multi-class problem with ordinal constraints. The challenge of designing accurate classifiers for ordinal regression generally increases with the number of classes involved, due to the large number of labeled patterns that are needed. The availability of ordinal class labels, however, are often costly to calibrate or difficult to obtain. Unlabeled patterns, on the other hand, often exist in much greater abundance and are freely available. To take benefits from the abundance of unlabeled patterns, we present a novel transductive learning paradigm for ordinal regression in this paper, namely Transductive Ordinal Regression (TOR). The key challenge of the present study lies in the precise estimation of both the ordinal class label of the unlabeled data and the decision functions of the ordinal classes, simultaneously. The core elements of the proposed TOR include an objective function that caters to several commonly used loss functions casted in transductive setting...
Nonparametric Predictive Regression
Ioannis Kasparis; Elena Andreou; Phillips, Peter C.B.
2012-01-01
A unifying framework for inference is developed in predictive regressions where the predictor has unknown integration properties and may be stationary or nonstationary. Two easily implemented nonparametric F-tests are proposed. The test statistics are related to those of Kasparis and Phillips (2012) and are obtained by kernel regression. The limit distribution of these predictive tests holds for a wide range of predictors including stationary as well as non-stationary fractional and near unit...
Hatzitaki, Vassilia; Konstadakos, Stylianos
2007-10-01
The effects of aging on the acquisition of a novel visuo-postural coordination task were addressed at two levels: (a) changes in the intersegmental coordination (local dynamics) (b) changes in the coupling of postural sway to the visual driving stimulus (global dynamics). Twelve elderly (age: 71.2 +/- 6.4 years; height: 169.3 +/- 3.8 cm; mass: 72.4 +/- 6.1 kg) and 12 young women (age: 27.1 +/- 4.9 years; height: 178.3 +/- 2.9 cm; mass: 56.7 +/- 4.1 kg) practiced a visually guided Weight-Shifting (WS) task while standing on a dual force platform. The participants were asked to keep the vertical force applied by each limb within a +/-30% force boundary that was visually specified by a target sine-wave signal. Practice consisted of three blocks of five trials performed in 1-day, followed by a block of five trials performed 24 h later. Ground reaction forces and segment (shank, pelvis, and upper trunk) angular kinematics were synchronously sampled through an A/D acquisition board and further analyzed employing spectral and coherence analysis. Elderly women had longer WS cycles, lower response gain, and higher within-trial variability, suggesting a weaker coupling between the visual stimulus and the response force. Spectral analysis of the ground reaction forces confirmed that regardless of age, visuo-postural coupling improved with practice. However, the recruitment of local degrees of freedom was different between the two age groups. With practice, young performers increased peak coherence between the pelvis and the upper trunk and reduced peak power of segment oscillations in the pitch direction. On the other hand, elderly women decreased active upper trunk rotation while shifting control to the lower limb. It is suggested that different functional coordination solutions are possible for attaining the same overall task goal. These solutions are determined by age-related constraints in the physiological systems supporting postural control.
Projection-type estimation for varying coefficient regression models
Lee, Young K; Park, Byeong U; 10.3150/10-BEJ331
2012-01-01
In this paper we introduce new estimators of the coefficient functions in the varying coefficient regression model. The proposed estimators are obtained by projecting the vector of the full-dimensional kernel-weighted local polynomial estimators of the coefficient functions onto a Hilbert space with a suitable norm. We provide a backfitting algorithm to compute the estimators. We show that the algorithm converges at a geometric rate under weak conditions. We derive the asymptotic distributions of the estimators and show that the estimators have the oracle properties. This is done for the general order of local polynomial fitting and for the estimation of the derivatives of the coefficient functions, as well as the coefficient functions themselves. The estimators turn out to have several theoretical and numerical advantages over the marginal integration estimators studied by Yang, Park, Xue and H\\"{a}rdle [J. Amer. Statist. Assoc. 101 (2006) 1212--1227].
... Anger Weight Management Weight Management Smoking and Weight Healthy Weight Loss Being Comfortable in Your Own Skin Your Weight Loss Expectations & Goals Healthier Lifestyle Healthier Lifestyle Physical Fitness Food & Nutrition Sleep, Stress & Relaxation Emotions & Relationships HealthyYouTXT ...
Hypotheses testing for fuzzy robust regression parameters
Energy Technology Data Exchange (ETDEWEB)
Kula, Kamile Sanli [Ahi Evran University, Department of Mathematics, 40200 Kirsehir (Turkey)], E-mail: sanli2004@hotmail.com; Apaydin, Aysen [Ankara University, Department of Statistics, 06100 Ankara (Turkey)], E-mail: apaydin@science.ankara.edu.tr
2009-11-30
The classical least squares (LS) method is widely used in regression analysis because computing its estimate is easy and traditional. However, LS estimators are very sensitive to outliers and to other deviations from basic assumptions of normal theory [Huynh H. A comparison of four approaches to robust regression. Psychol Bull 1982;92:505-12; Stephenson D. 2000. Available from: (http://folk.uib.no/ngbnk/kurs/notes/node38.html); Xu R, Li C. Multidimensional least-squares fitting with a fuzzy model. Fuzzy Sets and Systems 2001;119:215-23.]. If there exists outliers in the data set, robust methods are preferred to estimate parameters values. We proposed a fuzzy robust regression method by using fuzzy numbers when x is crisp and Y is a triangular fuzzy number and in case of outliers in the data set, a weight matrix was defined by the membership function of the residuals. In the fuzzy robust regression, fuzzy sets and fuzzy regression analysis was used in ranking of residuals and in estimation of regression parameters, respectively [Sanli K, Apaydin A. Fuzzy robust regression analysis based on the ranking of fuzzy sets. Inernat. J. Uncertainty Fuzziness and Knowledge-Based Syst 2008;16:663-81.]. In this study, standard deviation estimations are obtained for the parameters by the defined weight matrix. Moreover, we propose another point of view in hypotheses testing for parameters.
Relationship between Multiple Regression and Selected Multivariable Methods.
Schumacker, Randall E.
The relationship of multiple linear regression to various multivariate statistical techniques is discussed. The importance of the standardized partial regression coefficient (beta weight) in multiple linear regression as it is applied in path, factor, LISREL, and discriminant analyses is emphasized. The multivariate methods discussed in this paper…
[Understanding logistic regression].
El Sanharawi, M; Naudet, F
2013-10-01
Logistic regression is one of the most common multivariate analysis models utilized in epidemiology. It allows the measurement of the association between the occurrence of an event (qualitative dependent variable) and factors susceptible to influence it (explicative variables). The choice of explicative variables that should be included in the logistic regression model is based on prior knowledge of the disease physiopathology and the statistical association between the variable and the event, as measured by the odds ratio. The main steps for the procedure, the conditions of application, and the essential tools for its interpretation are discussed concisely. We also discuss the importance of the choice of variables that must be included and retained in the regression model in order to avoid the omission of important confounding factors. Finally, by way of illustration, we provide an example from the literature, which should help the reader test his or her knowledge.
Constrained Sparse Galerkin Regression
Loiseau, Jean-Christophe
2016-01-01
In this work, we demonstrate the use of sparse regression techniques from machine learning to identify nonlinear low-order models of a fluid system purely from measurement data. In particular, we extend the sparse identification of nonlinear dynamics (SINDy) algorithm to enforce physical constraints in the regression, leading to energy conservation. The resulting models are closely related to Galerkin projection models, but the present method does not require the use of a full-order or high-fidelity Navier-Stokes solver to project onto basis modes. Instead, the most parsimonious nonlinear model is determined that is consistent with observed measurement data and satisfies necessary constraints. The constrained Galerkin regression algorithm is implemented on the fluid flow past a circular cylinder, demonstrating the ability to accurately construct models from data.
Practical Session: Logistic Regression
Clausel, M.; Grégoire, G.
2014-12-01
An exercise is proposed to illustrate the logistic regression. One investigates the different risk factors in the apparition of coronary heart disease. It has been proposed in Chapter 5 of the book of D.G. Kleinbaum and M. Klein, "Logistic Regression", Statistics for Biology and Health, Springer Science Business Media, LLC (2010) and also by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr341.pdf). This example is based on data given in the file evans.txt coming from http://www.sph.emory.edu/dkleinb/logreg3.htm#data.
DEFF Research Database (Denmark)
Bache, Stefan Holst
A new and alternative quantile regression estimator is developed and it is shown that the estimator is root n-consistent and asymptotically normal. The estimator is based on a minimax ‘deviance function’ and has asymptotically equivalent properties to the usual quantile regression estimator. It is......, however, a different and therefore new estimator. It allows for both linear- and nonlinear model specifications. A simple algorithm for computing the estimates is proposed. It seems to work quite well in practice but whether it has theoretical justification is still an open question....
Ritz, Christian; Parmigiani, Giovanni
2009-01-01
R is a rapidly evolving lingua franca of graphical display and statistical analysis of experiments from the applied sciences. This book provides a coherent treatment of nonlinear regression with R by means of examples from a diversity of applied sciences such as biology, chemistry, engineering, medicine and toxicology.
Multiple linear regression analysis
Edwards, T. R.
1980-01-01
Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Adaptive metric kernel regression
DEFF Research Database (Denmark)
Goutte, Cyril; Larsen, Jan
2000-01-01
regression by minimising a cross-validation estimate of the generalisation error. This allows to automatically adjust the importance of different dimensions. The improvement in terms of modelling performance is illustrated on a variable selection task where the adaptive metric kernel clearly outperforms...
Software Regression Verification
2013-12-11
of recursive procedures. Acta Informatica , 45(6):403 – 439, 2008. [GS11] Benny Godlin and Ofer Strichman. Regression verifica- tion. Technical Report...functions. Therefore, we need to rede - fine m-term. – Mutual termination. If either function f or function f ′ (or both) is non- deterministic, then their
Seber, George A F
2012-01-01
Concise, mathematically clear, and comprehensive treatment of the subject.* Expanded coverage of diagnostics and methods of model fitting.* Requires no specialized knowledge beyond a good grasp of matrix algebra and some acquaintance with straight-line regression and simple analysis of variance models.* More than 200 problems throughout the book plus outline solutions for the exercises.* This revision has been extensively class-tested.
Energy Technology Data Exchange (ETDEWEB)
Van den Bergh, Laura, E-mail: laura.vandenbergh@uzleuven.be [Department of Radiation Oncology, University Hospitals Leuven, Leuven (Belgium); Koole, Michel [Department of Nuclear Medicine, University Hospitals Leuven, Leuven (Belgium); Isebaert, Sofie [Department of Radiation Oncology, University Hospitals Leuven, Leuven (Belgium); Joniau, Steven [Department of Urology, University Hospitals Leuven, Leuven (Belgium); Deroose, Christophe M. [Department of Nuclear Medicine, University Hospitals Leuven, Leuven (Belgium); Oyen, Raymond [Department of Radiology, University Hospitals Leuven, Leuven (Belgium); Lerut, Evelyne [Department of Histopathology, University Hospitals Leuven, Leuven (Belgium); Budiharto, Tom [Department of Radiation Oncology, University Hospitals Leuven, Leuven (Belgium); Mottaghy, Felix [Department of Nuclear Medicine, University Hospitals Leuven, Leuven (Belgium); Klinik fuer Nuklearmedizin, Universitaetsklinikum Aachen, Aachen (Germany); Bormans, Guy [Department of Nuclear Medicine, University Hospitals Leuven, Leuven (Belgium); Van Poppel, Hendrik [Department of Urology, University Hospitals Leuven, Leuven (Belgium); Haustermans, Karin [Department of Radiation Oncology, University Hospitals Leuven, Leuven (Belgium)
2012-08-01
Purpose: To investigate the additional value of {sup 11}C-choline positron emission tomography (PET)-computed tomography (CT) to T2-weighted (T2w) magnetic resonance imaging (MRI) for localization of intraprostatic tumor nodules. Methods and Materials: Forty-nine prostate cancer patients underwent T2w MRI and {sup 11}C-choline PET-CT before radical prostatectomy and extended lymphadenectomy. Tumor regions were outlined on the whole-mount histopathology sections and on the T2w MR images. Tumor localization was recorded in the basal, middle, and apical part of the prostate by means of an octant grid. To analyze {sup 11}C-choline PET-CT images, the same grid was used to calculate the standardized uptake values (SUV) per octant, after rigid registration with the T2w MR images for anatomic reference. Results: In total, 1,176 octants were analyzed. Sensitivity, specificity, and accuracy of T2w MRI were 33.5%, 94.6%, and 70.2%, respectively. For {sup 11}C-choline PET-CT, the mean SUV{sub max} of malignant octants was significantly higher than the mean SUV{sub max} of benign octants (3.69 {+-} 1.29 vs. 3.06 {+-} 0.97, p < 0.0001) which was also true for mean SUV{sub mean} values (2.39 {+-} 0.77 vs. 1.94 {+-} 0.61, p < 0.0001). A positive correlation was observed between SUV{sub mean} and absolute tumor volume (Spearman r = 0.3003, p = 0.0362). No correlation was found between SUVs and prostate-specific antigen, T-stage or Gleason score. The highest accuracy (61.1%) was obtained with a SUV{sub max} cutoff of 2.70, resulting in a sensitivity of 77.4% and a specificity of 44.9%. When both modalities were combined (PET-CT or MRI positive), sensitivity levels increased as a function of SUV{sub max} but at the cost of specificity. When only considering suspect octants on {sup 11}C-choline PET-CT (SUV{sub max} {>=} 2.70) and T2w MRI, 84.7% of these segments were in agreement with the gold standard, compared with 80.5% for T2w MRI alone. Conclusions: The additional value of {sup
Energy Technology Data Exchange (ETDEWEB)
Cai, Pei-Qiang; Wu, Yao-Pan; Xie, Chuan-Miao; Wu, Pei-Hong [Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou (China); Department of Medical Imaging and Interventional Radiology, Guangzhou (China); An, Xin [Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou (China); Department of Medical Oncology, Guangzhou (China); Qiu, Xue; Kong, Ling-Heng; Liu, Guo-Chen; Pan, Zhi-Zhong; Ding, Pei-Rong [Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou (China); Sun Yat-sen University Cancer Center, Department of Colorectal Surgery, Guangzhou (China)
2014-11-15
To determine diagnostic performance of simple measurements on diffusion-weighted MR imaging (DWI) for assessment of complete tumour response (CR) after neoadjuvant chemoradiotherapy (CRT) in patients with locally advanced rectal cancer (LARC) by signal intensity (SI) and apparent diffusion coefficient (ADC) measurements. Sixty-five patients with LARC who underwent neoadjuvant CRT and subsequent surgery were included. Patients underwent pre-CRT and post-CRT 3.0 T MRI. Regions of interest of the highest brightness SI were included in the tumour volume on post-CRT DWI to calculate the SI{sub lesion}, rSI, ADC{sub lesion} and rADC; diagnostic performance was compared by using the receiver operating characteristic (ROC) curves. In order to validate the accuracy and reproducibility of the current strategy, the same procedure was reproduced in 80 patients with LARC at 1.5 T MRI. Areas under the ROC curve for identification of a CR, based on SI{sub lesion}, rSI, ADC{sub lesion}, and rADC, respectively, were 0.86, 0.94, 0.66, and 0.71 at 3.0 T MRI, and 0.92, 0.91, 0.64, and 0.61 at 1.5 T MRI. Post-CRT DWI SI{sub lesion} and rSI provided high diagnostic performance in assessing CR and were significantly more accurate than ADC{sub lesion}, and rADC at 3.0 T MRI and 1.5 T MRI. (orig.)
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.
Heteroscedasticity checks for regression models
Institute of Scientific and Technical Information of China (English)
ZHU; Lixing
2001-01-01
［1］Carroll, R. J., Ruppert, D., Transformation and Weighting in Regression, New York: Chapman and Hall, 1988.［2］Cook, R. D., Weisberg, S., Diagnostics for heteroscedasticity in regression, Biometrika, 1988, 70: 1—10.［3］Davidian, M., Carroll, R. J., Variance function estimation, J. Amer. Statist. Assoc., 1987, 82: 1079—1091.［4］Bickel, P., Using residuals robustly I: Tests for heteroscedasticity, Ann. Statist., 1978, 6: 266—291.［5］Carroll, R. J., Ruppert, D., On robust tests for heteroscedasticity, Ann. Statist., 1981, 9: 205—209.［6］Eubank, R. L., Thomas, W., Detecting heteroscedasticity in nonparametric regression, J. Roy. Statist. Soc., Ser. B, 1993, 55: 145—155.［7］Diblasi, A., Bowman, A., Testing for constant variance in a linear model, Statist. and Probab. Letters, 1997, 33: 95—103.［8］Dette, H., Munk, A., Testing heteoscedasticity in nonparametric regression, J. R. Statist. Soc. B, 1998, 60: 693—708.［9］Müller, H. G., Zhao, P. L., On a semi-parametric variance function model and a test for heteroscedasticity, Ann. Statist., 1995, 23: 946—967.［10］Stute, W., Manteiga, G., Quindimil, M. P., Bootstrap approximations in model checks for regression, J. Amer. Statist. Asso., 1998, 93: 141—149.［11］Stute, W., Thies, G., Zhu, L. X., Model checks for regression: An innovation approach, Ann. Statist., 1998, 26: 1916—1939.［12］Shorack, G. R., Wellner, J. A., Empirical Processes with Applications to Statistics, New York: Wiley, 1986.［13］Efron, B., Bootstrap methods: Another look at the jackknife, Ann. Statist., 1979, 7: 1—26.［14］Wu, C. F. J., Jackknife, bootstrap and other re-sampling methods in regression analysis, Ann. Statist., 1986, 14: 1261—1295.［15］H rdle, W., Mammen, E., Comparing non-parametric versus parametric regression fits, Ann. Statist., 1993, 21: 1926—1947.［16］Liu, R. Y., Bootstrap procedures under some non-i.i.d. models, Ann. Statist., 1988, 16: 1696—1708.［17
一种基于局部加权均值的领域适应学习框架%A Local Weighted Mean Based Domain Adaptation Learning Framework
Institute of Scientific and Technical Information of China (English)
皋军; 黄丽莉; 孙长银
2013-01-01
Maximum mean discrepancy (MMD),as a criterion effectively and efficiently measuring the distribution discrepancy between source domains and target ones,has been successfully used.But it is a global measuring algorithm and to some extent only reflects the global distribution discrepancy between domains and the global structural difference.Therefore,we propose projected maximum local weighted mean discrepancy (PMLWD) scheme by with locality preserving ability integrating the theory and method of local weighted mean into the MMD.At the same time,we formulate in theory that the PMLWD is one of generalized algorithms of the MMD.Furthermore,on the basis of the PMLWD and by integrating classical learning theories,we present local weighted mean based domain adaptation learning framework (LDAF).Following the LDAF,we propose local weighted mean based multi-label classification domain adaptation learning algorithm (LDAF_MLC) and local weighted mean based domain adaptation supporting vector machine (LDAF_SVM).At last,tests on artificial data sets,high dimensional text data sets and face data sets show the LDAF methods are superior to other domain adaption ones.%最大均值差异(Maximum mean discrepancy,MMD)作为一种能有效度量源域和目标域分布差异的标准已被成功运用.然而,MMD作为一种全局度量方法一定程度上反映的是区域之间全局分布和全局结构上的差异.为此,本文通过引入局部加权均值的方法和理论到MMD中,提出一种具有局部保持能力的投影最大局部加权均值差异(Projected maximumlocal weighted mean discrepancy,PMLWD)度量,结合传统的学习理论提出基于局部加权均值的领域适应学习框架(Local weighted mean based domain adaptation learning framework,LDAF),在LDAF框架下,衍生出两种领域适应学习方法:LDAF_MLC和LDAF_SVM.最后,通过测试人工数据集、高维文本数据集和人脸数据集来表明LDAF比其他领域适应学习方法更具优势.
The Importance of Structure Coefficients in Interpreting Regression Research.
Heidgerken, Amanda D.
The paper stresses the importance of consulting beta weights and structure coefficients in the interpretation of regression results. The effects of multilinearity and suppressors and their effects on interpretation of beta weights are discussed. It is concluded that interpretations based on beta weights only can lead the unwary researcher to…
Low rank Multivariate regression
Giraud, Christophe
2010-01-01
We consider in this paper the multivariate regression problem, when the target regression matrix $A$ is close to a low rank matrix. Our primary interest in on the practical case where the variance of the noise is unknown. Our main contribution is to propose in this setting a criterion to select among a family of low rank estimators and prove a non-asymptotic oracle inequality for the resulting estimator. We also investigate the easier case where the variance of the noise is known and outline that the penalties appearing in our criterions are minimal (in some sense). These penalties involve the expected value of the Ky-Fan quasi-norm of some random matrices. These quantities can be evaluated easily in practice and upper-bounds can be derived from recent results in random matrix theory.
Subset selection in regression
Miller, Alan
2002-01-01
Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:A separate chapter on Bayesian methodsComplete revision of the chapter on estimationA major example from the field of near infrared spectroscopyMore emphasis on cross-validationGreater focus on bootstrappingStochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible Software available on the Internet for implementing many of the algorithms presentedMore examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting...
Classification and regression trees
Breiman, Leo; Olshen, Richard A; Stone, Charles J
1984-01-01
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
DEFF Research Database (Denmark)
Hansen, Henrik; Tarp, Finn
2001-01-01
. There are, however, decreasing returns to aid, and the estimated effectiveness of aid is highly sensitive to the choice of estimator and the set of control variables. When investment and human capital are controlled for, no positive effect of aid is found. Yet, aid continues to impact on growth via...... investment. We conclude by stressing the need for more theoretical work before this kind of cross-country regressions are used for policy purposes....
Robust Nonstationary Regression
1993-01-01
This paper provides a robust statistical approach to nonstationary time series regression and inference. Fully modified extensions of traditional robust statistical procedures are developed which allow for endogeneities in the nonstationary regressors and serial dependence in the shocks that drive the regressors and the errors that appear in the equation being estimated. The suggested estimators involve semiparametric corrections to accommodate these possibilities and they belong to the same ...
TWO REGRESSION CREDIBILITY MODELS
Directory of Open Access Journals (Sweden)
Constanţa-Nicoleta BODEA
2010-03-01
Full Text Available In this communication we will discuss two regression credibility models from Non – Life Insurance Mathematics that can be solved by means of matrix theory. In the first regression credibility model, starting from a well-known representation formula of the inverse for a special class of matrices a risk premium will be calculated for a contract with risk parameter θ. In the next regression credibility model, we will obtain a credibility solution in the form of a linear combination of the individual estimate (based on the data of a particular state and the collective estimate (based on aggregate USA data. To illustrate the solution with the properties mentioned above, we shall need the well-known representation theorem for a special class of matrices, the properties of the trace for a square matrix, the scalar product of two vectors, the norm with respect to a positive definite matrix given in advance and the complicated mathematical properties of conditional expectations and of conditional covariances.
Myers, Valerie H.; McVay, Megan A.; Champagne, Catherine M.; Hollis, Jack F.; Coughlin, Janelle W.; Funk, Kristine L.; Gullion, Christina M.; Jerome, Gerald J.; Loria, Catherine M.; Samuel-Hodge, Carmen D; Stevens, Victor J; Svetkey, Laura P; Brantley, Phillip J.
2012-01-01
Past studies have suggested that weight loss history is associated with subsequent weight loss. However, questions remain whether method and amount of weight lost in previous attempts impacts current weight loss efforts. This study utilized data from the Weight Loss Maintenance Trial to examine the association between weight loss history and weight loss outcomes in a diverse sample of high-risk individuals. Multivariate regression analysis was conducted to determine which specific aspects of ...
Institute of Scientific and Technical Information of China (English)
努丽曼古·阿不都克力木; 张晓帆; 陈川; 徐仕琪; 赵同阳
2012-01-01
加权Logistic回归是基于GIS成矿预测的主要方法之一,其模型是不同于线性模型的一种类型.它具有强大的空间分析功能、适用性强、不受任何独立条件的约束、预测结果更可靠,因此在矿产资源评价研究中得到了很多地质学家的青睐.以矿床模型和成矿理论为基础,加权Logistic回归分析模型在成矿预测中的应用主要包括三部分:加权Logistic回归模型的建立及其应用、成矿有利度综合评价、成矿远景区圈定.本文以中国—哈萨克斯坦边境地区扎尔玛—萨吾尔成矿带斑岩型铜矿为例,探讨了基于GIS的加权Logistic回归模型在成矿预测中的应用.%Weighted Logistic Regression is one of the main methods of mineral potential mapping. It is different from linear model. Because of its powerful spatial analysis function, strong adaptability, unconstrained by independent conditions, and more reliable prediction results, Weighted Logistic Regression is widely used by many geologists in mineral resources assessment. Based on the mineral deposit model and theory, Weighted Logistic Regression is consists of three parts: (1) Establishment of weighted logistic regression model for mineral potential mapping; (2 ) comprehensive evaluation of favorable degrees; (3 ) mineral potential mapping of study area. By the Weighted Logistic Regression model for mineral potential mapping, Zharma-Sawur Metallogic Belt which across border region of China and Kazakhstan is studied and mineral prospecting area of porphyry copper deposit is mapped. At the end, the availability of Weighted Logistic Regression Model for mineral potential mapping is discussed.
Gestational Weight Gain and Fetal Birth Weight in Rural Regions of Rasht/Iran
Directory of Open Access Journals (Sweden)
Zahra Panahandeh
2009-03-01
Full Text Available Objective: Proper nutrition during pregnancy is essential for optimal fetal growth. Investigation of the relation between pregnancy weight gain and birth weight in rural regions of Rasht, center of Guilan Province in Iran, was the purpose of this study. Methods: In this cohort study, prenatal data of 918 women who attended local health centers with singleton term pregnancies were recorded. Maternal demographic characteristics, anthropometric measurements, total pregnancy weight gain and birth weight were recorded by health workers. The women were stratified based on their pre-pregnancy body mass index (BMI into four groups: underweight women, women with normal weight, overweight women and obese women. The relation between weight gain and low birth weight (LBW, birth weight <2500 g and macrosomia (birth weight >4000 g was studied in these four groups. Data were analyzed using Chi-square test, independent t-test, Pearson correlation and logistic regression with 95% confidence intervals. Findings: More than 50% of underweight women and women with normal weight and almost 30% of overweight and obese women gained weight less than what is mentioned in the Institute of Medicine (IOM recommendations. The incidence rate of LBW was 7.1% and that of macrosomia was 5%. Mean weight gain of women with LBW was significantly less than mean weight gain of women who had an infant with a birth weight more than 2500 g (P=0.002. Women who gained weight less than the recommended range had higher rate of LBW in their infants (P=0.01 and the incidence of macrosomia in women with a weight gain above the recommended weight was higher than that in others (P=0.012. Pregnancy weight gain less than what is mentioned in the IOM guideline was the only predictor for LBW (OR=2.79, CI=1.16-6.73, P=0.02. Conclusion:Pregnancy weight gains less than what is mentioned in the IOM recommendation was a significant predictor of LBW, regardless of pre-pregnancy BMI.
... Health Information Weight Management English English Español Weight Management Obesity is a chronic condition that affects more ... Liver (NASH) Heart Disease & Stroke Sleep Apnea Weight Management Topics About Food Portions Bariatric Surgery for Severe ...
Uncertainty quantification in DIC with Kriging regression
Wang, Dezhi; DiazDelaO, F. A.; Wang, Weizhuo; Lin, Xiaoshan; Patterson, Eann A.; Mottershead, John E.
2016-03-01
A Kriging regression model is developed as a post-processing technique for the treatment of measurement uncertainty in classical subset-based Digital Image Correlation (DIC). Regression is achieved by regularising the sample-point correlation matrix using a local, subset-based, assessment of the measurement error with assumed statistical normality and based on the Sum of Squared Differences (SSD) criterion. This leads to a Kriging-regression model in the form of a Gaussian process representing uncertainty on the Kriging estimate of the measured displacement field. The method is demonstrated using numerical and experimental examples. Kriging estimates of displacement fields are shown to be in excellent agreement with 'true' values for the numerical cases and in the experimental example uncertainty quantification is carried out using the Gaussian random process that forms part of the Kriging model. The root mean square error (RMSE) on the estimated displacements is produced and standard deviations on local strain estimates are determined.
Modified Regression Correlation Coefficient for Poisson Regression Model
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).
Directory of Open Access Journals (Sweden)
Karim Hardani*
2012-05-01
Full Text Available A 10-month-old baby presented with developmental delay. He had flaccid paralysis on physical examination.An MRI of the spine revealed malformation of the ninth and tenth thoracic vertebral bodies with complete agenesis of the rest of the spine down that level. The thoracic spinal cord ends at the level of the fifth thoracic vertebra with agenesis of the posterior arches of the eighth, ninth and tenth thoracic vertebral bodies. The roots of the cauda equina appear tightened down and backward and ended into a subdermal fibrous fatty tissue at the level of the ninth and tenth thoracic vertebral bodies (closed meningocele. These findings are consistent with caudal regression syndrome.
Institute of Scientific and Technical Information of China (English)
李春发; 李勇; 谭洪玲; 王强
2011-01-01
揭示生态产业共生网络(EISN)中信息的传播规律对产业共生网络的稳定性以及抗毁性研究具有重要的意义.国内外学者对其研究多是基于无权无标度网络,由于现实中大部分产业共生网络都是加权网络,且多为加权局域网络,为了使研究结果更加符合现实客观规律,本文研究了信息在加权产业共生局域网络中的传播模型.通过改进初始条件,建立新的产业共生局域网络信息传播模型,以计算机仿真为主要手段,假定信息传播速度和产业共生网络中边上的权重成正比关系,研究结果表明:加权产业共生局域网络能够较为真实的反应现实EISN中信息传播规律,其无标度特性以及局域世界特性对信息在网络中传播具有重大影响.%Revealing the information dissemination rules of ecological industrial symbiosis network (EISN) has a vital significance on stability and anti-destroying ability of industrial symbiosis network. The studies of domestic and foreign scholars are mostly based on right scale-free network. In fact, most of the industrial symbiosis networks are weighted network, and more are weighted local area network. In order to make the results of the research more suitable to realistic objective laws, this paper studies the information transmission model in the weighted industrial symbiosis network. In the paper, through we establish the Information Spreading Model in Weighted Local-world Industrial Symbiosis Network, use computer simulation as the main means, assume information transmission speed and the weights of the edge of industrial symbiosis network are positive correlative, we get the results indicated that Weighted Local-world Industrial Symbiosis Network can more genuine response the real EISN information transmission law and Its scale-free characteristics and Weighted Local-world characteristics have significant effect on information transmission in the network.
Ackerman, Margareta; Branzei, Simina; Loker, David
2011-01-01
In this paper we investigate clustering in the weighted setting, in which every data point is assigned a real valued weight. We conduct a theoretical analysis on the influence of weighted data on standard clustering algorithms in each of the partitional and hierarchical settings, characterising the precise conditions under which such algorithms react to weights, and classifying clustering methods into three broad categories: weight-responsive, weight-considering, and weight-robust. Our analysis raises several interesting questions and can be directly mapped to the classical unweighted setting.
Regression Commonality Analysis: A Technique for Quantitative Theory Building
Nimon, Kim; Reio, Thomas G., Jr.
2011-01-01
When it comes to multiple linear regression analysis (MLR), it is common for social and behavioral science researchers to rely predominately on beta weights when evaluating how predictors contribute to a regression model. Presenting an underutilized statistical technique, this article describes how organizational researchers can use commonality…
Interpreting Bivariate Regression Coefficients: Going beyond the Average
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…
Directory of Open Access Journals (Sweden)
Rania A. Marouf
2015-09-01
Conclusion: The use of additional DWI yields better diagnostic accuracy than does use of conventional MR imaging alone in the evaluation of complete response to neoadjuvant chemo radiotherapy in patients with locally advanced rectal cancer.
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Zhou, J; Harb, J; Jawad, M; Yee, S; Schulze, D; Liang, J; Grills D, Yan [William Beaumont Hospital, Royal Oak, MI (United States)
2014-06-15
Purpose: In follow-up T2-weighted MR images of spinal tumor patients treated with stereotactic body radiation therapy (SBRT), high intensity features embedded in dark surroundings may suggest a local failure (LF). We investigated image intensity histogram in imaging features to predict LF and local control (LC). Methods: Sixty-seven spinal tumors were treated with SBRT at our institution with scheduled follow-up MR T2-weighted (TR 3200–6600ms; TE 75-132ms) imaging. The LF group included 10 tumors with 8.7 months median follow-up, while the LC group had 11 tumors with 24.1 months median follow-up. The follow-up images were fused to the planning CT. Image intensity histograms of the GTV were calculated. Voxels in greater than 90% (V90), 80% (V80), and peak (Vpeak) of the histogram were grouped into sub-ROIs to determine the best feature histogram. The intensity of each sub-ROI was evaluated using the mean T2-weighted signal ratio (intensity in sub-ROI / intensity in normal vertebrae). An ROC curve in predicting LF for each sub-ROI was calculated to determine the best feature histogram parameter for LF prediction. Results: Mean T2-weighted signal ratio in the LF group was significantly higher than that in the LC group for all sub-ROIs (1.1±0.4 vs. 0.7±0.2, 1.2±0.4 vs. 0.8±0.2, 1.4±0.5 vs. 0.8±0.2, for V90, V80, and Vpeak, p=0.02, 0.02, and 0.002, respectively). The corresponding areas-under-curve (AUC) of ROC were 0.78, 0.80, and 0.87, p=0.02, 0.03, 0.004, respectively. No correlation was found between T2-weighted signal ratio in Vpeak and follow-up time (Pearson's ρ=0.15). Conclusion: Increased T2-weighted signal can be used to identify local failure while decreased signal indicates local control after spinal SBRT. By choosing the best histogram parameter (here the Vpeak), the AUC of the ROC can be substantially improved, which implies reliable prediction of LC and LF. These results are being further studied and validated with large multi
Institute of Scientific and Technical Information of China (English)
WU An-Cai; XU Xin-Jian; WU Zhi-Xi; WANG Ying-Hai
2007-01-01
We investigate the dynamics of random walks on weighted networks. Assuming that the edge weight and the node strength are used as local information by a random walker. Two kinds of walks, weight-dependent walk and strength-dependent walk, are studied. Exact expressions for stationary distribution and average return time are derived and confirmed by computer simulations. The distribution of average return time and the mean-square that a weight-dependent walker can arrive at a new territory more easily than a strength-dependent one.
[Iris movement mediates pupillary membrane regression].
Morizane, Yuki
2007-11-01
In the course of mammalian lens development, a transient capillary meshwork called as the pupillary membrane (PM) forms. It is located in the pupil area to nourish the anterior surface of the lens, and then regresses to clear the optical path. Although the involvement of the apoptotic process has been reported in PM regression, the initiating factor remains unknown. We initially found that regression of the PM coincided with the development of iris motility, and that iris movement caused cessation and resumption of blood flow within the PM. Therefore, we investigated whether the development of the capacity of the iris to constrict and dilate can function as an essential signal that induces apoptosis in the PM. Continuous inhibition of iris movement with mydriatic agents suppressed apoptosis of the PM and resulted in the persistence of PM in rats. The distribution of apoptotic cells in the regressing PM was diffuse and showed no apparent localization. These results indicated that iris movement induced regression of the PM by changing the blood flow within it. This study suggests the importance of the physiological interactions between tissues-in this case, the iris and the PM-as a signal to advance vascular regression during organ development.
Recursive Algorithm For Linear Regression
Varanasi, S. V.
1988-01-01
Order of model determined easily. Linear-regression algorithhm includes recursive equations for coefficients of model of increased order. Algorithm eliminates duplicative calculations, facilitates search for minimum order of linear-regression model fitting set of data satisfactory.
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Jorge Agudelo
2011-10-01
Full Text Available El análisis de las externalidades en el sector inmobiliario ha atraído desde hace varios años la atención de los investigadores suscitando una gran cantidad de estudios al respecto. En este artículo se utilizan modelos econométricos tradicionales, de la econometría espacial y de regresión ponderada geográficamente, para analizar y comparar a la luz de estos modelos la influencia que tiene en los precios de las viviendas la existencia de una estación del metro en San Javier ubicada en el centro occidente de la ciudad de Medellín. El principal hallazgo en este estudio es que la presencia de la estación del metro tiene una influencia positiva en los precios de las viviendas localizadas en un radio de 600 metros alrededor de la estación; sin embargo, las viviendas cercanas a las vías de acceso del metro a la estación presentan un importante decremento en sus precios. AbstractThe analysis of externalities in real state has been matter of study during the past few years. In this paper we use both conventional and spatial econometric model, as well as geographically weighted regression models, to measure the effect of the San Javier Metro Station (in Medellín, Colombia on the housing prices of the surrounding area. The main finding of this study is that the metro station has a positive impact on the prices of houses located within a radius of 600 meter from the station. However, the railroad track accessing the station has a negative impact on housing prices located nearby.The analysis of externalities in real state has been matter of study during the past few years. In this paper we use both conventional and spatial econometric model, as well as geographically weighted regression models, to measure the effect of the San Javier Metro Station (in Medellín, Colombia on the housing prices of the surrounding area.The main finding of this study is that the metro station has a positive impact on the prices of houses located within a radius
Regression Test Selection for C# Programs
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Nashat Mansour
2009-01-01
Full Text Available We present a regression test selection technique for C# programs. C# is fairly new and is often used within the Microsoft .Net framework to give programmers a solid base to develop a variety of applications. Regression testing is done after modifying a program. Regression test selection refers to selecting a suitable subset of test cases from the original test suite in order to be rerun. It aims to provide confidence that the modifications are correct and did not affect other unmodified parts of the program. The regression test selection technique presented in this paper accounts for C#.Net specific features. Our technique is based on three phases; the first phase builds an Affected Class Diagram consisting of classes that are affected by the change in the source code. The second phase builds a C# Interclass Graph (CIG from the affected class diagram based on C# specific features. In this phase, we reduce the number of selected test cases. The third phase involves further reduction and a new metric for assigning weights to test cases for prioritizing the selected test cases. We have empirically validated the proposed technique by using case studies. The empirical results show the usefulness of the proposed regression testing technique for C#.Net programs.
Carroll, Suzanne J; Paquet, Catherine; Howard, Natasha J; Coffee, Neil T; Adams, Robert J; Taylor, Anne W; Niyonsenga, Theo; Daniel, Mark
2017-02-02
Individual-level health outcomes are shaped by environmental risk conditions. Norms figure prominently in socio-behavioural theories yet spatial variations in health-related norms have rarely been investigated as environmental risk conditions. This study assessed: 1) the contributions of local descriptive norms for overweight/obesity and dietary behaviour to 10-year change in glycosylated haemoglobin (HbA1c), accounting for food resource availability; and 2) whether associations between local descriptive norms and HbA1c were moderated by food resource availability. HbA1c, representing cardiometabolic risk, was measured three times over 10 years for a population-based biomedical cohort of adults in Adelaide, South Australia. Residential environmental exposures were defined using 1600 m participant-centred road-network buffers. Local descriptive norms for overweight/obesity and insufficient fruit intake (proportion of residents with BMI ≥ 25 kg/m(2) [n = 1890] or fruit intake of descriptive norm and either fast-food or healthful food resource availability, with area-level education and individual-level covariates (age, sex, employment status, education, marital status, and smoking status). Interactions between local descriptive norms and food resource availability were tested. HbA1c concentration rose over time. Local descriptive norms for overweight/obesity and insufficient fruit intake predicted greater rates of increase in HbA1c. Neither fast-food nor healthful food resource availability were associated with change in HbA1c. Greater healthful food resource availability reduced the rate of increase in HbA1c concentration attributed to the overweight/obesity norm. Local descriptive health-related norms, not food resource availability, predicted 10-year change in HbA1c. Null findings for food resource availability may reflect a sufficiency or minimum threshold level of resources such that availability poses no barrier to obtaining healthful or
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Ha, Hong Il [University of Ulsan College of Medicine, Asan Medical Center, Department of Radiology and Research Institute of Radiology, Seoul (Korea, Republic of); Hallym University Medical Center, Hallym University Sacred Heart Hospital, Department of Radiology, Anyang-si, Gyeonggi-do (Korea, Republic of); Kim, Ah Young; Park, Seong Ho; Ha, Hyun Kwon [University of Ulsan College of Medicine, Asan Medical Center, Department of Radiology and Research Institute of Radiology, Seoul (Korea, Republic of); Yu, Chang Sik [University of Ulsan College of Medicine, Asan Medical Center, Department of Colon and Rectal Surgery, Seoul (Korea, Republic of)
2013-12-15
To evaluate DW MR tumour volumetry and post-CRT ADC in rectal cancer as predicting factors of CR using high b values to eliminate perfusion effects. One hundred rectal cancer patients who underwent 1.5-T rectal MR and DW imaging using three b factors (0, 150, and 1,000 s/mm{sup 2}) were enrolled. The tumour volumes of T2-weighted MR and DW images and pre- and post-CRT ADC{sub 150-1000} were measured. The diagnostic accuracy of post-CRT ADC, T2-weighted MR, and DW tumour volumetry was compared using ROC analysis. DW MR tumour volumetry was superior to T2-weighted MR volumetry comparing the CR and non-CR groups (P < 0.001). Post-CRT ADC showed a significant difference between the CR and non-CR groups (P = 0.001). The accuracy of DW tumour volumetry (A{sub z} = 0.910) was superior to that of T2-weighed MR tumour volumetry (A{sub z} = 0.792) and post-CRT ADC (A{sub z} = 0.705) in determining CR (P = 0.015). Using a cutoff value for the tumour volume reduction rate of more than 86.8 % on DW MR images, the sensitivity and specificity for predicting CR were 91.4 % and 80 %, respectively. DW MR tumour volumetry after CRT showed significant superiority in predicting CR compared with T2-weighted MR images and post-CRT ADC. (orig.)
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Salvatore Francesco Carbone
2014-01-01
Full Text Available Objectives. To assess the diagnostic performance of diffusion-weighted MR imaging (DWI in patients affected by prostatic fossa (PF relapse after radical prostatectomy (RP for prostatic carcinoma (PC. Methods. Twenty-seven patients showing a nodular lesion in the PF at T2-weighted MR imaging after RP, with diagnosis of PC relapse established by biopsy or PSA determinations, were investigated by DWI. Two readers evaluated the DWI results in consensus and the apparent diffusion coefficient (ADC of the nodules, separately; a mean value was obtained (ADCm. Results. Relapses did not significantly differ in size in respect of postsurgical benign nodules. The DWI qualitative evaluation showed sensitivity, specificity, accuracy, ppv, and npv values, respectively, of 83.3%, 88.9%, 85.2%, 93.7%, and 72.7% (100%, 87.5%, 95.6%, 93.7%, and 100%, for nodules >6 mm. The intraclass correlation coefficient (ICC for ADC evaluation between the two readers was 0.852 (95% CI 0.661–0.935; P=0.0001. The ADCm values for relapses and benign nodules were, respectively, 0.98±0.21×10−3 mm2/sec and 1.24±0.32×10−3 mm2/sec (P=0.006. Sensitivity, specificity, accuracy, ppv and npv of ADCm were, respectively, 77.8%, 88.9%, 81.8%, 93.3%, and 66.7% (93.3%, 87.5%, 85.4%, 93.3%, and 87.5% for nodules >6 mm. Conclusions. Diffusion-weighted MR imaging is a promising tool in the management of a hyperintense nodule detected by T2-weighted sequences. This might have a relevant importance in contouring radiotherapy treatment volumes.
A simplified electrophoretic system for determining molecular weights of proteins.
Manwell, C
1977-09-01
Electrophoresis of 31 different proteins in commercially prepared polyacrylamide gradient gels, Gradipore, yields a linear relationship between a hypothetical limiting pore size (the reciprocal of a limiting gel concentration, GL) and the cube root of the mol.wt., over the range 13 500-9000 000. A regression analysis of these data reveals that 98.6% of all variability in 1/GL is explained by the molecular weight, and this degree of accuracy compares favourably with existing methods for the determination of molecular weight by retardation of mobility in polyacrylamide. This new procedure has the additional advantages that molecular-weight standards can be obtained from readily available body fluids or tissue extracts by localizing enzymes and other proteins by standard histochemical methods, and that the same electrophoretic system can be used in determining molecular weights as is used in routine surveys of populations for individual and species variation in protein heterogeneity.
Regression in autistic spectrum disorders.
Stefanatos, Gerry A
2008-12-01
A significant proportion of children diagnosed with Autistic Spectrum Disorder experience a developmental regression characterized by a loss of previously-acquired skills. This may involve a loss of speech or social responsitivity, but often entails both. This paper critically reviews the phenomena of regression in autistic spectrum disorders, highlighting the characteristics of regression, age of onset, temporal course, and long-term outcome. Important considerations for diagnosis are discussed and multiple etiological factors currently hypothesized to underlie the phenomenon are reviewed. It is argued that regressive autistic spectrum disorders can be conceptualized on a spectrum with other regressive disorders that may share common pathophysiological features. The implications of this viewpoint are discussed.
... obese. Achieving a healthy weight can help you control your cholesterol, blood pressure and blood sugar. It ... use more calories than you eat. A weight-control strategy might include Choosing low-fat, low-calorie ...
Linear regression in astronomy. I
Isobe, Takashi; Feigelson, Eric D.; Akritas, Michael G.; Babu, Gutti Jogesh
1990-01-01
Five methods for obtaining linear regression fits to bivariate data with unknown or insignificant measurement errors are discussed: ordinary least-squares (OLS) regression of Y on X, OLS regression of X on Y, the bisector of the two OLS lines, orthogonal regression, and 'reduced major-axis' regression. These methods have been used by various researchers in observational astronomy, most importantly in cosmic distance scale applications. Formulas for calculating the slope and intercept coefficients and their uncertainties are given for all the methods, including a new general form of the OLS variance estimates. The accuracy of the formulas was confirmed using numerical simulations. The applicability of the procedures is discussed with respect to their mathematical properties, the nature of the astronomical data under consideration, and the scientific purpose of the regression. It is found that, for problems needing symmetrical treatment of the variables, the OLS bisector performs significantly better than orthogonal or reduced major-axis regression.
DEFF Research Database (Denmark)
Ackerman, Margareta; Ben-David, Shai; Branzei, Simina
2012-01-01
We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights.We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both...... the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify...
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Shin, Y.R. [The Catholic University of Korea, Department of Radiology, Seoul St. Mary' s Hospital, College of Medicine, 222, Banpo-daero, Seocho-gu, Seoul (Korea, Republic of); The Catholic University of Korea, Department of Radiology, Incheon St. Mary' s Hospital, College of Medicine, Bupyeong 6-dong, Bupyeong-gu, Incheon (Korea, Republic of); Rha, S.E.; Choi, B.G.; Oh, S.N.; Park, M.Y.; Byun, J.Y. [The Catholic University of Korea, Department of Radiology, Seoul St. Mary' s Hospital, College of Medicine, 222, Banpo-daero, Seocho-gu, Seoul (Korea, Republic of)
2013-04-15
To compare three-dimensional (3D) T2-weighted turbo spin-echo (TSE) with multiplanar two-dimensional (2D) T2-weighted TSE for the evaluation of invasive cervical carcinoma. Seventy-five patients with cervical carcinoma underwent MRI of the pelvis at 3.0 T, using both 5-mm-thick multiplanar 2D (total acquisition time = 12 min 25 s) and 1-mm-thick coronal 3D T2-weighted TSE sequences (7 min 20 s). Quantitative analysis of signal-to-noise ratio (SNR) and qualitative analysis of image quality were performed. Local-regional staging was performed in 45 patients who underwent radical hysterectomy. The estimated SNR of cervical carcinoma and the relative tumour contrast were significantly higher on 3D imaging (P < 0.0001). Tumour conspicuity was better with the 3D sequence, but the sharpness of tumour margin was better with the 2D sequence. No significant difference in overall image quality was noted between the two sequences (P = 0.38). There were no significant differences in terms of the diagnostic accuracy, sensitivity, and specificity of parametrial invasion, vaginal invasion, and lymph node metastases. Multiplanar reconstruction 3D T2-weighted imaging is largely equivalent to 2D T2-weighted imaging for overall image quality and staging accuracy of cervical carcinoma with a shorter MR data acquisition, but has limitations with regard to the sharpness of the tumour margin. circle 3D T2-weighted MR sequence is equivalent to 2D for cervical carcinoma staging. (orig.)
Novel algorithm for constructing support vector machine regression ensemble
Institute of Scientific and Technical Information of China (English)
Li Bo; Li Xinjun; Zhao Zhiyan
2006-01-01
A novel algorithm for constructing support vector machine regression ensemble is proposed. As to regression prediction, support vector machine regression(SVMR) ensemble is proposed by resampling from given training data sets repeatedly and aggregating several independent SVMRs, each of which is trained to use a replicated training set. After training, several independently trained SVMRs need to be aggregated in an appropriate combination manner. Generally, the linear weighting is usually used like expert weighting score in Boosting Regression and it is without optimization capacity. Three combination techniques are proposed, including simple arithmetic mean,linear least square error weighting and nonlinear hierarchical combining that uses another upper-layer SVMR to combine several lower-layer SVMRs. Finally, simulation experiments demonstrate the accuracy and validity of the presented algorithm.
... baby, taken just after he or she is born. A low birth weight is less than 5.5 pounds. A high ... weight is more than 8.8 pounds. A low birth weight baby can be born too small, too early (premature), or both. This ...
Directory of Open Access Journals (Sweden)
Fen Wei
2016-01-01
Full Text Available In order to sufficiently capture the useful fault-related information available in the multiple vibration sensors used in rotation machinery, while concurrently avoiding the introduction of the limitation of dimensionality, a new fault diagnosis method for rotation machinery based on supervised second-order tensor locality preserving projection (SSTLPP and weighted k-nearest neighbor classifier (WKNNC with an assembled matrix distance metric (AMDM is presented. Second-order tensor representation of multisensor fused conditional features is employed to replace the prevailing vector description of features from a single sensor. Then, an SSTLPP algorithm under AMDM (SSTLPP-AMDM is presented to realize dimensional reduction of original high-dimensional feature tensor. Compared with classical second-order tensor locality preserving projection (STLPP, the SSTLPP-AMDM algorithm not only considers both local neighbor information and class label information but also replaces the existing Frobenius distance measure with AMDM for construction of the similarity weighting matrix. Finally, the obtained low-dimensional feature tensor is input into WKNNC with AMDM to implement the fault diagnosis of the rotation machinery. A fault diagnosis experiment is performed for a gearbox which demonstrates that the second-order tensor formed multisensor fused fault data has good results for multisensor fusion fault diagnosis and the formulated fault diagnosis method can effectively improve diagnostic accuracy.
Institute of Scientific and Technical Information of China (English)
吴杰; 李梁; 王华奎
2011-01-01
针对Euclide,定位算法中定位精度及覆盖率受描节点密度影响较大的问题,提出一种改进的节点定位算法.根据节点初始定位精度及测距精度提出一种新的加权方法.定位后的节点升级为辅助信标点.未知节点根据更新的锚节点位置信息循环求精.仿真表明该定位系统既能提高定位覆盖率又能减少定位累积误差,从而提高整个网络的定位精度.%Aiming at the problem that anchor ratio has a strong impact on localization error and coverage in Euclidean algorithm, an improved localization algorithm is proposed.According to the initial node position accuracy and measurement accuracy,a new method for weighted is presented.The orphan node becomes assistant beacon node to aid localization after being localized.And the localization accuracy of other nodes is obtained by iterative refinements.Simulation results demonstrate that the position cumulative and average error are both lower,but the coverage is higher.
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.
Dyk, Pawel; Jiang, Naomi; Sun, Baozhou; DeWees, Todd A; Fowler, Kathryn J; Narra, Vamsi; Garcia-Ramirez, Jose L; Schwarz, Julie K; Grigsby, Perry W
2014-11-15
Magnetic resonance imaging/diffusion weighted-imaging (MRI/DWI)-guided high-dose-rate (HDR) brachytherapy and (18)F-fluorodeoxyglucose (FDG) - positron emission tomography/computed tomography (PET/CT)-guided intensity modulated radiation therapy (IMRT) for the definitive treatment of cervical cancer is a novel treatment technique. The purpose of this study was to report our analysis of dose-volume parameters predicting gross tumor volume (GTV) control. We analyzed the records of 134 patients with International Federation of Gynecology and Obstetrics stages IB1-IVB cervical cancer treated with combined MRI-guided HDR and IMRT from July 2009 to July 2011. IMRT was targeted to the metabolic tumor volume and lymph nodes by use of FDG-PET/CT simulation. The GTV for each HDR fraction was delineated by use of T2-weighted or apparent diffusion coefficient maps from diffusion-weighted sequences. The D100, D90, and Dmean delivered to the GTV from HDR and IMRT were summed to EQD2. One hundred twenty-five patients received all irradiation treatment as planned, and 9 did not complete treatment. All 134 patients are included in this analysis. Treatment failure in the cervix occurred in 24 patients (18.0%). Patients with cervix failures had a lower D100, D90, and Dmean than those who did not experience failure in the cervix. The respective doses to the GTV were 41, 58, and 136 Gy for failures compared with 67, 99, and 236 Gy for those who did not experience failure (PD100, D90, and Dmean doses required for ≥90% local control to be 69, 98, and 260 Gy (P<.001). Total dose delivered to the GTV from combined MRI-guided HDR and PET/CT-guided IMRT is highly correlated with local tumor control. The findings can be directly applied in the clinic for dose adaptation to maximize local control. Copyright © 2014 Elsevier Inc. All rights reserved.
Time-adaptive quantile regression
DEFF Research Database (Denmark)
Møller, Jan Kloppenborg; Nielsen, Henrik Aalborg; Madsen, Henrik
2008-01-01
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...... 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...... 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....
Linear regression in astronomy. II
Feigelson, Eric D.; Babu, Gutti J.
1992-01-01
A wide variety of least-squares linear regression procedures used in observational astronomy, particularly investigations of the cosmic distance scale, are presented and discussed. The classes of linear models considered are (1) unweighted regression lines, with bootstrap and jackknife resampling; (2) regression solutions when measurement error, in one or both variables, dominates the scatter; (3) methods to apply a calibration line to new data; (4) truncated regression models, which apply to flux-limited data sets; and (5) censored regression models, which apply when nondetections are present. For the calibration problem we develop two new procedures: a formula for the intercept offset between two parallel data sets, which propagates slope errors from one regression to the other; and a generalization of the Working-Hotelling confidence bands to nonstandard least-squares lines. They can provide improved error analysis for Faber-Jackson, Tully-Fisher, and similar cosmic distance scale relations.
Comparison of Four Methods for Weighting Multiple Predictors.
Aamodt, Michael G.; Kimbrough, Wilson W.
1985-01-01
Four methods were used to weight predictors associated with a Resident Assistant job: (1) rank order weights; (2) unit weights; (3) critical incident weights; and (4) regression weights. A cross-validation was also done. Most weighting methods were highly related. No method was superior in terms of protection from validity shrinkage. (GDC)
Polynomial Regression on Riemannian Manifolds
Hinkle, Jacob; Fletcher, P Thomas; Joshi, Sarang
2012-01-01
In this paper we develop the theory of parametric polynomial regression in Riemannian manifolds and Lie groups. We show application of Riemannian polynomial regression to shape analysis in Kendall shape space. Results are presented, showing the power of polynomial regression on the classic rat skull growth data of Bookstein as well as the analysis of the shape changes associated with aging of the corpus callosum from the OASIS Alzheimer's study.
Evaluating Differential Effects Using Regression Interactions and Regression Mixture Models
Van Horn, M. Lee; Jaki, Thomas; Masyn, Katherine; Howe, George; Feaster, Daniel J.; Lamont, Andrea E.; George, Melissa R. W.; Kim, Minjung
2015-01-01
Research increasingly emphasizes understanding differential effects. This article focuses on understanding regression mixture models, which are relatively new statistical methods for assessing differential effects by comparing results to using an interactive term in linear regression. The research questions which each model answers, their…
Marital status and body weight, weight perception, and weight management among U.S. adults.
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.
Functional data analysis of generalized regression quantiles
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.
Quantile regression theory and applications
Davino, Cristina; Vistocco, Domenico
2013-01-01
A guide to the implementation and interpretation of Quantile Regression models This book explores the theory and numerous applications of quantile regression, offering empirical data analysis as well as the software tools to implement the methods. The main focus of this book is to provide the reader with a comprehensivedescription of the main issues concerning quantile regression; these include basic modeling, geometrical interpretation, estimation and inference for quantile regression, as well as issues on validity of the model, diagnostic tools. Each methodological aspect is explored and
Business applications of multiple regression
Richardson, Ronny
2015-01-01
This second edition of Business Applications of Multiple Regression describes the use of the statistical procedure called multiple regression in business situations, including forecasting and understanding the relationships between variables. The book assumes a basic understanding of statistics but reviews correlation analysis and simple regression to prepare the reader to understand and use multiple regression. The techniques described in the book are illustrated using both Microsoft Excel and a professional statistical program. Along the way, several real-world data sets are analyzed in deta
Forecasting of Households Consumption Expenditure with Nonparametric Regression: The Case of Turkey
Directory of Open Access Journals (Sweden)
Aydin Noyan
2016-11-01
Full Text Available The relationship between household income and expenditure is important for understanding how the shape of the economic dynamics of the households. In this study, the relationship between household consumption expenditure and household disposable income were analyzed by Locally Weighted Scatterplot Smoothing Regression which is a nonparametric method using R programming. This study aimed to determine relationship between variables directly, unlike making any assumptions are commonly used as in the conventional parametric regression. According to the findings, effect on expenditure with increasing of income and household size together increased rapidly at first, and then speed of increase decreased. This increase can be explained by having greater compulsory consumption expenditure relatively in small households. Besides, expenditure is relatively higher in middle and high income levels according to low income level. However, the change in expenditure is limited in middle and is the most limited in high income levels when household size changes.
Demonstration of a Fiber Optic Regression Probe
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
Institute of Scientific and Technical Information of China (English)
张春慧; 陈美招; 郑荣宝
2015-01-01
进行滑坡的风险评估与区划研究,能够为决策者科学制定防灾减灾政策提供帮助. 在外业调查及相关研究基础上,选择地形、岩性、植被、土地利用、降水、断裂带和人类活动等11个因子作为评价指标,采用Logistic回归-加权支持向量机模型进行滑坡的风险评价,划分5种风险等级类型,最后采取ROC曲线进行模型精度检验.结果表明,花都区梯面镇大部分区域、花东镇和赤坭镇部分地区是滑坡灾害风险较高的区域,评价结果与65个滑坡灾害存量数据空间分布相吻合;风险等级中很低、较低、中等、较高和很高区域面积比例分别为 28. 19%、31. 31%、25. 54%、11. 73%和3. 24%;受试者工作特证( ROC)曲线的精度验证表明,Logistic回归-加权支持向量机模型能有效进行该区域的滑坡风险评价,且具有较好的评价精度、分类能力和客观性.%Landslide is one of the three major natural hazards in China. It is, therefore, very important to study how to perform landslide risk assessment and zoning. The related studies may provide policymakers with theoretic basis in formula-ting strategies and policies for control of landslides. On the basis of the field surveys and relevant researches already done, 11 factors, such as terrain, lithology, vegetation, land use, precipitation, fault, human activities, etc. have beer used as evaluation indexes in performing landslide risk assessment for Huadu District with the aid of the logistic regression-weighted support vector machine model. Landslide risks in the region were sorted into 5 grades, and in the end the model in fitting accuracy with ROC curve has been verified. Results show that the risk was quite high in a large portion of Timian Town, and a certain portion of Huadong and Chini towns, which is spatially consistent with distribution of the 65 landslide disaster inventory data; The regions, very low, low, moderate, high and very high in risk
Institute of Scientific and Technical Information of China (English)
赵增玉; 陈火根; 潘懋; 贾根; 李向前; 徐士银; 郭刚; 张祥云
2016-01-01
Application of the Weighted Logistic Regression model in prediction of volcanic rock type Copper deposits in the Middle part of Ning-Wu Basin is studied. First, the geological setting of ore-forming processes is analyzed. Three kinds of factors including geological body, structure and wall rock alteration are extracted based on the spatial distribution of copper deposits from the geologic map. Then, the spatial relationships between Copper mineral occurrence and each evidence factor are analyzed. It is suggested that Niangniangshan and Gushan volcanic edifice play an important role in spatial distributions of volcanic rock-hosted Copper deposits. The ten evidence raster layers including Longwangshan Formation, Gushan Formation, trachyte porphyry of Gushan volcanic edifice, monzonite porphyry of Niangniangshan volcanic edifice, buffers of the structure lines with NE, NW and EW trending, and the alteration areas of chalcopyrite, silicide and Limonite are selected. Finally, metallogenic probabilities are calculated using the Weighted Logistic Regression model. Four ore-forming prospects, including P1, P2, P3 and P4, are indicated based on the geological conditions of metallogenesis and model results. Among these prospecting areas, P1, P2 and P3, which are controlled by Niangniangshan and Gushan volcanic edifice, are spread in the northeast direction. P4 extends in the west-east direction and is controlled by Longwangshan volcanic edifice. The copper ore bodies are already found in these prospecting areas, suggesting that the results should be generally reliable.%文中探讨了加权Logistic回归模型在宁芜盆地中段火山岩型铜矿预测中的应用.首先,结合研究区的成矿地质背景,提取地质体、构造、围岩蚀变三大类证据因子;其次,分析各证据因子与铜矿点之间的空间关系,认为姑山旋回、娘娘山旋回火山机构控制了本区火山岩型铜矿的空间分布,根据计算结果,选取与火山岩型铜矿密
Testing discontinuities in nonparametric regression
Dai, Wenlin
2017-01-19
In nonparametric regression, it is often needed to detect whether there are jump discontinuities in the mean function. In this paper, we revisit the difference-based method in [13 H.-G. Müller and U. Stadtmüller, Discontinuous versus smooth regression, Ann. Stat. 27 (1999), pp. 299–337. doi: 10.1214/aos/1018031100
Logistic Regression: Concept and Application
Cokluk, Omay
2010-01-01
The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…
Energy Technology Data Exchange (ETDEWEB)
Somoye, Gbolahan; Parkin, David [Ward 42, Aberdeen Royal Infirmary, Aberdeen (United Kingdom); Harry, Vanessa [Royal Marsden NHS Foundation Trust, London (United Kingdom); Semple, Scott [Queen' s Medical Research Institute, Centre for Cardiovascular Science, Clinical Research Imaging Centre, Edinburgh (United Kingdom); Plataniotis, George [Musgrove Park Hospital, Taunton and Somerset NHS Foundation, Taunton (United Kingdom); Scott, Neil [University of Aberdeen, Section of Population Health, Aberdeen (United Kingdom); Gilbert, Fiona J. [University of Cambridge, Radiology Department, Box 218, Cambridge (United Kingdom)
2012-11-15
To assess the predictive value of diffusion weighted imaging (DWI) for survival in women treated for advanced cancer of the cervix with concurrent chemo-radiotherapy. Twenty women treated for advanced cancer of the cervix were recruited and followed up for a median of 26 (range <1 to 43) months. They each had DWI performed before treatment, 2 weeks after beginning therapy (midtreatment) and at the end of treatment. Apparent diffusion coefficient (ADC) values were calculated from regions of interest (ROI). All participants were reviewed for follow-up data. ADC values were compared with mortality status (Mann-Whitney test). Time to progression and overall survival were assessed (Kaplan-Meier survival graphs). There were 14 survivors. The median midtreatment ADC was statistically significantly higher in those alive compared to the non-survivors, 1.55 and 1.36 (x 10{sup -3}/mm{sup 2}/s), respectively, P = 0.02. The median change in ADC 14 days after treatment commencement was significantly higher in the alive group compared to non-survivors, 0.28 and 0.14 (x 10{sup -3}/mm{sup 2}/s), respectively, P = 0.02. There was no evidence of a difference between survivors and non-survivors for pretreatment baseline or post-therapy ADC values. Functional DWI early in the treatment of advanced cancer of the cervix may provide useful information in predicting survival. (orig.)
Institute of Scientific and Technical Information of China (English)
李尧尧; 廖红云; 曾孝平; 吴小林
2011-01-01
Concentric anchor beacons (CAB) localization algorithm is a range -free wireless sensor networks localization algorithm. Comparing with the traditional range localization algorithm, although it can reduce the nodes energy consumption, the localization accuracy is no better than traditional range localization approaches. In order to further improve the accuracy of localizing nodes, an improved weighted centroid algorithm based on CAB is proposed after analyzing the radio propagation path loss. First, the process of this improved algorithm is introduced. Then, the influence of communication radius on the accuracy of this new localization algorithm was simulated. After that, the accuracy of this localization algorithm were compared with the existing several kinds of range - free localization methods. Simulation results show that this new algorithm has a higher positioning accuracy and not sensitive to communication radius, and it has some value in productive practice.%研究无线传感器定位准确性问题,针对测量位置节点信息,为了提高无线传感器网络的定位精度,采用同心圆定位算法(CAB)是一种免测距的无线传感器定位算法,相比于传统的测距方法能降低节点的能量消耗,但是定位精度却不及传统的测距定位方法.提出在同心圆定位算法(CAB)的基础上,通过分析无线电传播路径损耗采用了一种加权同心圆定位算法.给出了算法的流程,仿真分析了通信半径对新算法定位精度的影响,比较了算法定位精度与现有的几种免测距定位方法的定位精度.仿真结果表明,改进算法有较高的定位精度而且对距离不敏感,对实际工程提供应用价值.
Regression Testing Cost Reduction Suite
Directory of Open Access Journals (Sweden)
Mohamed Alaa El-Din
2014-08-01
Full Text Available The estimated cost of software maintenance exceeds 70 percent of total software costs [1], and large portion of this maintenance expenses is devoted to regression testing. Regression testing is an expensive and frequently executed maintenance activity used to revalidate the modified software. Any reduction in the cost of regression testing would help to reduce the software maintenance cost. Test suites once developed are reused and updated frequently as the software evolves. As a result, some test cases in the test suite may become redundant when the software is modified over time since the requirements covered by them are also covered by other test cases. Due to the resource and time constraints for re-executing large test suites, it is important to develop techniques to minimize available test suites by removing redundant test cases. In general, the test suite minimization problem is NP complete. This paper focuses on proposing an effective approach for reducing the cost of regression testing process. The proposed approach is applied on real-time case study. It was found that the reduction in cost of regression testing for each regression testing cycle is ranging highly improved in the case of programs containing high number of selected statements which in turn maximize the benefits of using it in regression testing of complex software systems. The reduction in the regression test suite size will reduce the effort and time required by the testing teams to execute the regression test suite. Since regression testing is done more frequently in software maintenance phase, the overall software maintenance cost can be reduced considerably by applying the proposed approach.
Institute of Scientific and Technical Information of China (English)
林伟; 黎灿兵; 唐升卫
2011-01-01
Based on the local-world power grid evolving model, this paper selected network growing point via new approach of the combination of the gravity method and the random location approach, and presented a weighted local-world evolving network model considering network growing point for complex power grid which concentrated on the location of the substation and the amount of power capacity.The analysis of node degree distribution curve and the weight distribution curve indicates that new model can get the degree distribution of power law and node weight distribution of power law tail.Presented a simulation example in which different probability was illustrated for selecting network growing point with gravity method and random location approach, the node degree distribution curve would display a certain degree of scale-free properties, and the variation of probability would only impact on the tail of degree distribution curve.For the increasing influence of the gravity method, the weight of the node continues to enhance as the network growing time lasts and the distortion of the tail of the weight distribution curve reflects the violation of average distribution.%以变电站选址和定容为出发点,在局域世界电力网络演化模型的基础上,将重心选址法和随机选址法结合应用到网络生长点的选取中,提出了一种基于网络生长点的加权局域世界电力网络演化模型.对新模型的节点度分布和权重分布分析表明,新模型可以得到幂律分布的度分布和具有幂律尾的节点权重分布.通过仿真证实了重心法和随机法以不同的概率选取网络生长点时,节点度分布曲线仍然表现出一定的无标度特性,并且此概率的变化只是在度分布曲线尾部对度分布有影响;随着重心法因素的加强,部分节点随着网络生长时间的增长,节点权重越来越大,节点权重分布曲线的尾部畸变,不服从平均分布.
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.
Institute of Scientific and Technical Information of China (English)
陶莉莉; 钟伟民; 罗娜; 钱锋
2012-01-01
针对软测量建模过程中数据可能存在粗大误差以及粗差数据对模型的性能产生的影响,提出了一种基于粗差判别的自适应加权最小二乘支持向量机回归方法(WLS-SVM).该方法首先根据3δ法则检测出样本中的显著误差并加以剔除,然后根据样本误差的大小自适应地调整权值,使得非显著误差对模型性能的影响大大降低.另外,由于最小二乘支持向量机的正则化参数和核宽度参数对模型的拟合精度和泛化能力有较大的影响,一般依靠经验和试算的方法进行估计,耗时且不准确,本文将模型的参数作为进化算法的优化问题,应用自适应免疫算法(AIGA)对参数进行优化选择.仿真实验表明,该方法对非线性系统的建模具有很好的效果.同时,将该方法应用于工业PX氧化建模过程中动力学参数的估计中,结果表明,基于粗差判别的参数优化自适应最小二乘支持向量机预测精度高,取得了较好的效果.%The presence of gross errors can corrupt a model's performance,giving undesirable results. A novel weighted least square support vector machine regression (WLS-SVM) is proposed,which combines gross error detection and adaptive weight value for the training sample. First,the 3δ principle is applied to detect the gross error. Second,the initial weight is obtained according to the fitting error of each sample. Then,an adaptive immune algorithm (AIGA) is applied to obtain the optimal parameters of the WLS-SVM. To illustrate the performance of the WLS-SVM,simulation experiment is designed to produce the training sample. The results showed that the predicting performance of AIGA-WLS-SVM is the best. Furthermore,the AIGA-WLS-SVM method was applied to estimate the rate constants of an industrial p-xylene oxidation model,and the satisfactory result was obtained.
Rank regression: an alternative regression approach for data with outliers.
Chen, Tian; Tang, Wan; Lu, Ying; Tu, Xin
2014-10-01
Linear regression models are widely used in mental health and related health services research. However, the classic linear regression analysis assumes that the data are normally distributed, an assumption that is not met by the data obtained in many studies. One method of dealing with this problem is to use semi-parametric models, which do not require that the data be normally distributed. But semi-parametric models are quite sensitive to outlying observations, so the generated estimates are unreliable when study data includes outliers. In this situation, some researchers trim the extreme values prior to conducting the analysis, but the ad-hoc rules used for data trimming are based on subjective criteria so different methods of adjustment can yield different results. Rank regression provides a more objective approach to dealing with non-normal data that includes outliers. This paper uses simulated and real data to illustrate this useful regression approach for dealing with outliers and compares it to the results generated using classical regression models and semi-parametric regression models.
Geometric Properties of AR（q） Nonlinear Regression Models
Institute of Scientific and Technical Information of China (English)
LIUYing-ar; WEIBo-cheng
2004-01-01
This paper is devoted to a study of geometric properties of AR(q) nonlinear regression models. We present geometric frameworks for regression parameter space and autoregression parameter space respectively based on the weighted inner product by fisher information matrix. Several geometric properties related to statistical curvatures are given for the models. The results of this paper extended the work of Bates & Watts(1980,1988)[1.2] and Seber & Wild (1989)[3].
Multiple Kernel Spectral Regression for Dimensionality Reduction
Directory of Open Access Journals (Sweden)
Bing Liu
2013-01-01
Full Text Available Traditional manifold learning algorithms, such as locally linear embedding, Isomap, and Laplacian eigenmap, only provide the embedding results of the training samples. To solve the out-of-sample extension problem, spectral regression (SR solves the problem of learning an embedding function by establishing a regression framework, which can avoid eigen-decomposition of dense matrices. Motivated by the effectiveness of SR, we incorporate multiple kernel learning (MKL into SR for dimensionality reduction. The proposed approach (termed MKL-SR seeks an embedding function in the Reproducing Kernel Hilbert Space (RKHS induced by the multiple base kernels. An MKL-SR algorithm is proposed to improve the performance of kernel-based SR (KSR further. Furthermore, the proposed MKL-SR algorithm can be performed in the supervised, unsupervised, and semi-supervised situation. Experimental results on supervised classification and semi-supervised classification demonstrate the effectiveness and efficiency of our algorithm.
ORDINAL REGRESSION FOR INFORMATION RETRIEVAL
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
This letter presents a new discriminative model for Information Retrieval (IR), referred to as Ordinal Regression Model (ORM). ORM is different from most existing models in that it views IR as ordinal regression problem (i.e. ranking problem) instead of binary classification. It is noted that the task of IR is to rank documents according to the user information needed, so IR can be viewed as ordinal regression problem. Two parameter learning algorithms for ORM are presented. One is a perceptron-based algorithm. The other is the ranking Support Vector Machine (SVM). The effectiveness of the proposed approach has been evaluated on the task of ad hoc retrieval using three English Text REtrieval Conference (TREC) sets and two Chinese TREC sets. Results show that ORM significantly outperforms the state-of-the-art language model approaches and OKAPI system in all test sets; and it is more appropriate to view IR as ordinal regression other than binary classification.
Multiple Regression and Its Discontents
Snell, Joel C.; Marsh, Mitchell
2012-01-01
Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.
Multiple Regression and Its Discontents
Snell, Joel C.; Marsh, Mitchell
2012-01-01
Multiple regression is part of a larger statistical strategy originated by Gauss. The authors raise questions about the theory and suggest some changes that would make room for Mandelbrot and Serendipity.
Regression methods for medical research
Tai, Bee Choo
2013-01-01
Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures.The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the
Forecasting with Dynamic Regression Models
Pankratz, Alan
2012-01-01
One of the most widely used tools in statistical forecasting, single equation regression models is examined here. A companion to the author's earlier work, Forecasting with Univariate Box-Jenkins Models: Concepts and Cases, the present text pulls together recent time series ideas and gives special attention to possible intertemporal patterns, distributed lag responses of output to input series and the auto correlation patterns of regression disturbance. It also includes six case studies.
Wrong Signs in Regression Coefficients
McGee, Holly
1999-01-01
When using parametric cost estimation, it is important to note the possibility of the regression coefficients having the wrong sign. A wrong sign is defined as a sign on the regression coefficient opposite to the researcher's intuition and experience. Some possible causes for the wrong sign discussed in this paper are a small range of x's, leverage points, missing variables, multicollinearity, and computational error. Additionally, techniques for determining the cause of the wrong sign are given.
From Rasch scores to regression
DEFF Research Database (Denmark)
Christensen, Karl Bang
2006-01-01
Rasch models provide a framework for measurement and modelling latent variables. Having measured a latent variable in a population a comparison of groups will often be of interest. For this purpose the use of observed raw scores will often be inadequate because these lack interval scale propertie....... This paper compares two approaches to group comparison: linear regression models using estimated person locations as outcome variables and latent regression models based on the distribution of the score....
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.
Energy Technology Data Exchange (ETDEWEB)
Dyk, Pawel; Jiang, Naomi; Sun, Baozhou; DeWees, Todd A. [Department of Radiation Oncology, Washington University School of Medicine, St Louis, Missouri (United States); Fowler, Kathryn J.; Narra, Vamsi [Department of Diagnostic Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Missouri (United States); Garcia-Ramirez, Jose L.; Schwarz, Julie K. [Department of Radiation Oncology, Washington University School of Medicine, St Louis, Missouri (United States); Grigsby, Perry W., E-mail: pgrigsby@wustl.edu [Department of Radiation Oncology, Washington University School of Medicine, St Louis, Missouri (United States); Division of Nuclear Medicine, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Missouri (United States); Division of Gynecologic Oncology, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, Missouri (United States); Alvin J. Siteman Cancer Center, Washington University School of Medicine, St Louis, Missouri (United States)
2014-11-15
Purpose: Magnetic resonance imaging/diffusion weighted-imaging (MRI/DWI)-guided high-dose-rate (HDR) brachytherapy and {sup 18}F-fluorodeoxyglucose (FDG) — positron emission tomography/computed tomography (PET/CT)-guided intensity modulated radiation therapy (IMRT) for the definitive treatment of cervical cancer is a novel treatment technique. The purpose of this study was to report our analysis of dose-volume parameters predicting gross tumor volume (GTV) control. Methods and Materials: We analyzed the records of 134 patients with International Federation of Gynecology and Obstetrics stages IB1-IVB cervical cancer treated with combined MRI-guided HDR and IMRT from July 2009 to July 2011. IMRT was targeted to the metabolic tumor volume and lymph nodes by use of FDG-PET/CT simulation. The GTV for each HDR fraction was delineated by use of T2-weighted or apparent diffusion coefficient maps from diffusion-weighted sequences. The D100, D90, and Dmean delivered to the GTV from HDR and IMRT were summed to EQD2. Results: One hundred twenty-five patients received all irradiation treatment as planned, and 9 did not complete treatment. All 134 patients are included in this analysis. Treatment failure in the cervix occurred in 24 patients (18.0%). Patients with cervix failures had a lower D100, D90, and Dmean than those who did not experience failure in the cervix. The respective doses to the GTV were 41, 58, and 136 Gy for failures compared with 67, 99, and 236 Gy for those who did not experience failure (P<.001). Probit analysis estimated the minimum D100, D90, and Dmean doses required for ≥90% local control to be 69, 98, and 260 Gy (P<.001). Conclusions: Total dose delivered to the GTV from combined MRI-guided HDR and PET/CT-guided IMRT is highly correlated with local tumor control. The findings can be directly applied in the clinic for dose adaptation to maximize local control.
Calugi, Simona; Marchesini, Giulio; El Ghoch, Marwan; Gavasso, Ilaria; Dalle Grave, Riccardo
2017-01-01
Conflicting evidence exists as to whether cognitive mechanisms contribute to weight loss and maintenance. To assess the influence of weight-loss expectations on weight loss, and of weight-loss satisfaction on weight maintenance, in individuals with severe obesity. A randomized controlled trial comparing two types of energy-restricted diets (high protein vs high carbohydrate) combined with weight-loss cognitive behavioral therapy, conducted over 51 weeks and divided into two phases: weight-loss phase (3 weeks of inpatient treatment and 24 weeks of outpatient treatment) and weight maintenance phase (24 weeks of outpatient treatment). Eighty-eight participants with severe obesity (mean age=46.7 years and mean body mass index=45.6), referred to an eating and weight disorders clinical service, were studied. Body weight was assessed at baseline, and after 3, 27 (end of weight-loss phase), and 51 weeks (end of weight maintenance phase). Weight loss expectations were assessed at the time of enrollment, and weight-loss satisfaction was assessed after 27 weeks. The relationship between weight-loss expectations and weight loss was assessed using a linear mixed model. The association between weight-loss satisfaction and final outcomes was tested by linear regression. The two groups had similar weight-loss expectations and satisfaction, and their results were therefore pooled. In general, the total amount of expected weight loss (in kilograms), but not the percentage of expected weight loss, predicted weight loss, and both satisfaction with weight loss and the amount of weight lost (in kilograms) were independent predictors of weight maintenance. Higher expected weight loss improves weight loss, and both the total amount of weight lost and satisfaction with weight loss are associated with weight-loss maintenance at 1-year follow-up. Copyright © 2017 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.
Institute of Scientific and Technical Information of China (English)
赖永剑; 贺祥民
2016-01-01
Based on provincial data from 1998 to 2012,the article studies the market segmentation impact on the regional environmental total factor productivity through the geographically weighted regression model, which features the spatial heterogeneity. The study finds that market segmentation has no completely negative but different effects on the regional environmental total factor productivity. In the areas with lower degree of market segmentation,market segmentation benefits the total factor productivity; while in the areas with higher degree of market segmentation,market segmentation has negative effect on the total factor productivity;and the higher degree of market segmentation,the greater negative effect.Further study finds that,the spatial heterogeneity of the impact of market segmentation on environmental total factor productivity is mainly reflected in environmental technology progress rate.%采用1998—2012年的省级数据，利用能考虑空间异质性的地理加权回归模型，研究了市场分割对地区环境全要素生产率的影响。研究发现市场分割对地区环境全要素生产率并非存在完全的负向效应，而是存在差异化的影响，在市场分割程度较低的地区，市场分割有利于环境全要素生产率提升；但是，在市场分割程度较高的地区，市场分割却对环境全要素生产率产生了负面影响，且随着市场分割的加剧，表现出同步的上升趋势。进一步研究发现，市场分割对环境全要素生产率影响的空间异质性主要由环境技术进步率体现出来。
On a method of perfect regression using sinusoidal expansion
Sinha, Nilotpal Kanti
2011-01-01
We present a new method of weighted least square regression that gives a curve of fit with any desired degree of accuracy for a given set of data points. By applying this iterative process infinitely, we show that every finite set of coplanar points can be expanded as a sinusoidal series in infinitely many ways. Thus, given any set of finite data points, we can obtain infinitely many perfect regression curves which give a perfect match between the given data points and the values given by the regression.
Univariate graduation of mortality by local polynomial regression
Tomas, J.
2012-01-01
Life tables are used to describe the one-year probability of death within a well defined population as a function of attained age. These probabilities play an important role in the determination of premium rates and reserves in life insurance. The crude estimates on which life tables are based might
Damci, Taner; Emral, Rifat; Svendsen, Anne Louise; Balkir, Tanzer; Vora, Jiten
2014-07-21
The purpose of this analysis is to evaluate the safety and effectiveness of insulin initiation with once-daily insulin detemir (IDet) or insulin glargine (IGlar) in real-life clinical practice in Turkish patients with type 2 diabetes mellitus (T2DM). This was a 24-week multinational observational study of insulin initiation in patients with T2DM. The Turkish cohort (n = 2886) included 2395 patients treated with IDet and 491 with IGlar. The change in glycosylated haemoglobin (HbA1c) from the pre-insulin levels was -2.21% [95% confidence interval (CI) -2.32, -2.09] in the IDet group and -1.88% [95% CI -2.17, -1.59] in the IGlar group at the final visit. The incidence rate of minor hypoglycaemia increased in both groups from the pre-insulin to the final visit (+0.66 and +2.23 events per patient year in the IDet and IGlar groups, respectively). Weight change in the IDet group was -0.23 kg [95% CI -0.49, 0.02 kg], and +1.55 kg [95% CI 1.11, 2.00 kg] in the IGlar group. Regression analysis with adjustment for previously identified confounders (age, gender, duration of diabetes, body mass index, previous history of hypoglycaemia, microvascular disease, number and change in oral anti-diabetic drug therapy, HbA1c at baseline and insulin dose) identified an independent effect of insulin type (IDet versus IGlar) with a risk of at least one episode of hypoglycaemia (odds ratio (OR): 0.33 [95% CI 0.21, 0.52], p insulin analogues, IDet and IGlar, were associated with clinically significant glycaemic improvements. A lower risk of minor hypoglycaemia and greater odds of weight loss ≥1 kg was observed with IDet compared with IGlar. NCT00825643 and NCT00740519.
Sparse Volterra and Polynomial Regression Models: Recoverability and Estimation
Kekatos, Vassilis
2011-01-01
Volterra and polynomial regression models play a major role in nonlinear system identification and inference tasks. Exciting applications ranging from neuroscience to genome-wide association analysis build on these models with the additional requirement of parsimony. This requirement has high interpretative value, but unfortunately cannot be met by least-squares based or kernel regression methods. To this end, compressed sampling (CS) approaches, already successful in linear regression settings, can offer a viable alternative. The viability of CS for sparse Volterra and polynomial models is the core theme of this work. A common sparse regression task is initially posed for the two models. Building on (weighted) Lasso-based schemes, an adaptive RLS-type algorithm is developed for sparse polynomial regressions. The identifiability of polynomial models is critically challenged by dimensionality. However, following the CS principle, when these models are sparse, they could be recovered by far fewer measurements. ...
A Matlab program for stepwise regression
Directory of Open Access Journals (Sweden)
Yanhong Qi
2016-03-01
Full Text Available The stepwise linear regression is a multi-variable regression for identifying statistically significant variables in the linear regression equation. In present study, we presented the Matlab program of stepwise regression.
Spatial regression analysis of traffic crashes in Seoul.
Rhee, Kyoung-Ah; Kim, Joon-Ki; Lee, Young-ihn; Ulfarsson, Gudmundur F
2016-06-01
Traffic crashes can be spatially correlated events and the analysis of the distribution of traffic crash frequency requires evaluation of parameters that reflect spatial properties and correlation. Typically this spatial aspect of crash data is not used in everyday practice by planning agencies and this contributes to a gap between research and practice. A database of traffic crashes in Seoul, Korea, in 2010 was developed at the traffic analysis zone (TAZ) level with a number of GIS developed spatial variables. Practical spatial models using available software were estimated. The spatial error model was determined to be better than the spatial lag model and an ordinary least squares baseline regression. A geographically weighted regression model provided useful insights about localization of effects. The results found that an increased length of roads with speed limit below 30 km/h and a higher ratio of residents below age of 15 were correlated with lower traffic crash frequency, while a higher ratio of residents who moved to the TAZ, more vehicle-kilometers traveled, and a greater number of access points with speed limit difference between side roads and mainline above 30 km/h all increased the number of traffic crashes. This suggests, for example, that better control or design for merging lower speed roads with higher speed roads is important. A key result is that the length of bus-only center lanes had the largest effect on increasing traffic crashes. This is important as bus-only center lanes with bus stop islands have been increasingly used to improve transit times. Hence the potential negative safety impacts of such systems need to be studied further and mitigated through improved design of pedestrian access to center bus stop islands.
Predicting residue-wise contact orders in proteins by support vector regression
Directory of Open Access Journals (Sweden)
Burrage Kevin
2006-10-01
Full Text Available Abstract Background The residue-wise contact order (RWCO describes the sequence separations between the residues of interest and its contacting residues in a protein sequence. It is a new kind of one-dimensional protein structure that represents the extent of long-range contacts and is considered as a generalization of contact order. Together with secondary structure, accessible surface area, the B factor, and contact number, RWCO provides comprehensive and indispensable important information to reconstructing the protein three-dimensional structure from a set of one-dimensional structural properties. Accurately predicting RWCO values could have many important applications in protein three-dimensional structure prediction and protein folding rate prediction, and give deep insights into protein sequence-structure relationships. Results We developed a novel approach to predict residue-wise contact order values in proteins based on support vector regression (SVR, starting from primary amino acid sequences. We explored seven different sequence encoding schemes to examine their effects on the prediction performance, including local sequence in the form of PSI-BLAST profiles, local sequence plus amino acid composition, local sequence plus molecular weight, local sequence plus secondary structure predicted by PSIPRED, local sequence plus molecular weight and amino acid composition, local sequence plus molecular weight and predicted secondary structure, and local sequence plus molecular weight, amino acid composition and predicted secondary structure. When using local sequences with multiple sequence alignments in the form of PSI-BLAST profiles, we could predict the RWCO distribution with a Pearson correlation coefficient (CC between the predicted and observed RWCO values of 0.55, and root mean square error (RMSE of 0.82, based on a well-defined dataset with 680 protein sequences. Moreover, by incorporating global features such as molecular weight and
Institute of Scientific and Technical Information of China (English)
杨茂; 季本明
2015-01-01
准确的风电功率实时预测，对电力系统安全、经济高效运行有着重要作用。基于相空间重构理论，提出了一种局域一阶加权法，以马式距离作为邻近相点的判据，权值的选择以邻近相点和参考相点的距离为依据，构建预测模型。以东北某风电场的实测风电功率时间序列为例，进行仿真分析，结果表明模型可有效地提高预测精度。%The accurate wind power forecasting is important to the safe and efficient operation of electrical power system. Base on the phase space reconstruction theory, the weighted-one rank local region method is presented. In the method,the Mahalanobis distance is taken as the criterion for determining the adjacent phase points,and it is also determines the weight of points. Taking the wind power time series of a wind farm in Northeast China as an example,the simulation analysis shows that the model is effective.
Biplots in Reduced-Rank Regression
Braak, ter C.J.F.; Looman, C.W.N.
1994-01-01
Regression problems with a number of related response variables are typically analyzed by separate multiple regressions. This paper shows how these regressions can be visualized jointly in a biplot based on reduced-rank regression. Reduced-rank regression combines multiple regression and principal c
Interpretation of Standardized Regression Coefficients in Multiple Regression.
Thayer, Jerome D.
The extent to which standardized regression coefficients (beta values) can be used to determine the importance of a variable in an equation was explored. The beta value and the part correlation coefficient--also called the semi-partial correlation coefficient and reported in squared form as the incremental "r squared"--were compared for…
Inferential Models for Linear Regression
Directory of Open Access Journals (Sweden)
Zuoyi Zhang
2011-09-01
Full Text Available Linear regression is arguably one of the most widely used statistical methods in applications. However, important problems, especially variable selection, remain a challenge for classical modes of inference. This paper develops a recently proposed framework of inferential models (IMs in the linear regression context. In general, an IM is able to produce meaningful probabilistic summaries of the statistical evidence for and against assertions about the unknown parameter of interest and, moreover, these summaries are shown to be properly calibrated in a frequentist sense. Here we demonstrate, using simple examples, that the IM framework is promising for linear regression analysis --- including model checking, variable selection, and prediction --- and for uncertain inference in general.
[Is regression of atherosclerosis possible?].
Thomas, D; Richard, J L; Emmerich, J; Bruckert, E; Delahaye, F
1992-10-01
Experimental studies have shown the regression of atherosclerosis in animals given a cholesterol-rich diet and then given a normal diet or hypolipidemic therapy. Despite favourable results of clinical trials of primary prevention modifying the lipid profile, the concept of atherosclerosis regression in man remains very controversial. The methodological approach is difficult: this is based on angiographic data and requires strict standardisation of angiographic views and reliable quantitative techniques of analysis which are available with image processing. Several methodologically acceptable clinical coronary studies have shown not only stabilisation but also regression of atherosclerotic lesions with reductions of about 25% in total cholesterol levels and of about 40% in LDL cholesterol levels. These reductions were obtained either by drugs as in CLAS (Cholesterol Lowering Atherosclerosis Study), FATS (Familial Atherosclerosis Treatment Study) and SCOR (Specialized Center of Research Intervention Trial), by profound modifications in dietary habits as in the Lifestyle Heart Trial, or by surgery (ileo-caecal bypass) as in POSCH (Program On the Surgical Control of the Hyperlipidemias). On the other hand, trials with non-lipid lowering drugs such as the calcium antagonists (INTACT, MHIS) have not shown significant regression of existing atherosclerotic lesions but only a decrease on the number of new lesions. The clinical benefits of these regression studies are difficult to demonstrate given the limited period of observation, relatively small population numbers and the fact that in some cases the subjects were asymptomatic. The decrease in the number of cardiovascular events therefore seems relatively modest and concerns essentially subjects who were symptomatic initially. The clinical repercussion of studies of prevention involving a single lipid factor is probably partially due to the reduction in progression and anatomical regression of the atherosclerotic plaque
Nonparametric regression with filtered data
Linton, Oliver; Nielsen, Jens Perch; Van Keilegom, Ingrid; 10.3150/10-BEJ260
2011-01-01
We present a general principle for estimating a regression function nonparametrically, allowing for a wide variety of data filtering, for example, repeated left truncation and right censoring. Both the mean and the median regression cases are considered. The method works by first estimating the conditional hazard function or conditional survivor function and then integrating. We also investigate improved methods that take account of model structure such as independent errors and show that such methods can improve performance when the model structure is true. We establish the pointwise asymptotic normality of our estimators.
Logistic regression for circular data
Al-Daffaie, Kadhem; Khan, Shahjahan
2017-05-01
This paper considers the relationship between a binary response and a circular predictor. It develops the logistic regression model by employing the linear-circular regression approach. The maximum likelihood method is used to estimate the parameters. The Newton-Raphson numerical method is used to find the estimated values of the parameters. A data set from weather records of Toowoomba city is analysed by the proposed methods. Moreover, a simulation study is considered. The R software is used for all computations and simulations.
Quasi-least squares regression
Shults, Justine
2014-01-01
Drawing on the authors' substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression-a computational approach for the estimation of correlation parameters within the framework of generalized estimating equations (GEEs). The authors present a detailed evaluation of QLS methodology, demonstrating the advantages of QLS in comparison with alternative methods. They describe how QLS can be used to extend the application of the traditional GEE approach to the analysis of unequally spaced longitu
Predictive regressions for macroeconomic data
Fukang Zhu; Zongwu Cai; Liang Peng
2014-01-01
Researchers have constantly asked whether stock returns can be predicted by some macroeconomic data. However, it is known that macroeconomic data may exhibit nonstationarity and/or heavy tails, which complicates existing testing procedures for predictability. In this paper we propose novel empirical likelihood methods based on some weighted score equations to test whether the monthly CRSP value-weighted index can be predicted by the log dividend-price ratio or the log earnings-price ratio. Th...
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
Institute of Scientific and Technical Information of China (English)
杨月全; 韩飞; 金露; 倪春波; 曹志强; 张天平
2012-01-01
To accelerate the learning speed of robots for multi-robot systems and make full use of experience and results of other robots in the communication domain, two kinds of multi-robot learning strategies based on the local weighted k-nearest neighbor temporal difference (kNN-TD) algorithm are proposed. Without consideration of time delays during the communications of robots, based on the method of local weighted kNN-TD state selection by using environment sense information and task destination information, the optimal iteration of Q value table of a robot is updated by the employment of comparison and analysis of Q value tables of itself and other communicating robots. After that, asynchronous interaction reinforcement learning schemes are presented in the case of global communication and local communication in the working environment, respectively. Finally, the simulations verify the effectiveness and efficiency of the proposed strategy.%针对多机器人系统的增强学习问题,为提高机器人的学习速度和充分利用通信范围内其他机器人的增强学习的经验和结果,给出了2类基于局部加权k近邻时间差分的多机器人系统的交互式学习策略.对于机器人之间通信无时滞情形,基于环境感测和任务信息状态描述的局部加权k近邻状态选择方法,机器人通过对自身和通信范围内其他机器人Q值表的比较和分析,对其自身的Q值表进行优化迭代更新.在此基础上,分别给出了基于全局通信条件下和局部通信条件下多机器人系统的异步的互增强学习方案.最后,通过仿真实验进一步验证了所提方案的可行性和有效性.
Estimating liver weight of adults by body weight and gender
Institute of Scientific and Technical Information of China (English)
See Ching Chan; Chi Leung Liu; Chung Mau Lo; Banny K Lam; Evelyn W Lee; Yik Wong; Sheung Tat Fan
2006-01-01
AIM: To estimate the standard liver weight for assessing adequacies of graft size in live donor liver transplantation and remnant liver in major hepatectomy for cancer.METHODS: In this study, anthropometric data of body weight and body height were tested for a correlation with liver weight in 159 live liver donors who underwent donor right hepatectomy including the middle hepatic vein. Liver weights were calculated from the right lobe graft weight obtained at the back table, divided by the proportion of the right lobe on the computed tomography.RESULTS: The subjects, all Chinese, had a mean age of 35.8 ± 10.5 years, and a female to male ratio of 118:41. The mean volume of the right lobe was 710.14 ±131.46 mL and occupied 64.55%±4.47% of the whole liver on computed tomography. Right lobe weighed 598.90±117.39 g and the estimated liver weight was 927.54 ± 168.78 g. When body weight and body height were subjected to multiple stepwise linear regression analysis, body height was found to be insignificant. Females of the same body weight had a slightly lower liver weight. A formula based on body weight and gender was derived: Estimated standard liver weight (g) = 218 + BW (kg) x 12.3 + genderx 51 (R2 = 0.48)(female = 0, male = 1). Based on the anthropometric data of these 159 subjects, liver weights were calculated using previously published formulae derived from studies on Caucasian, Japanese, Korean, and Chinese.All formulae overestimated liver weights compared to this formula. The Japanese formula overestimated the estimated standard liver weight (ESLW) for adults less than 60 kg.CONCLUSION: A formula applicable to Chinese males and females is available. A formula for individual races appears necessary.
Interpreting Multiple Linear Regression: A Guidebook of Variable Importance
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…
On the null distribution of Bayes factors in linear regression
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...
Regression of lumbar disk herniation
Directory of Open Access Journals (Sweden)
G. Yu Evzikov
2015-01-01
Full Text Available Compression of the spinal nerve root, giving rise to pain and sensory and motor disorders in the area of its innervation is the most vivid manifestation of herniated intervertebral disk. Different treatment modalities, including neurosurgery, for evolving these conditions are discussed. There has been recent evidence that spontaneous regression of disk herniation can regress. The paper describes a female patient with large lateralized disc extrusion that has caused compression of the nerve root S1, leading to obvious myotonic and radicular syndrome. Magnetic resonance imaging has shown that the clinical manifestations of discogenic radiculopathy, as well myotonic syndrome and morphological changes completely regressed 8 months later. The likely mechanism is inflammation-induced resorption of a large herniated disk fragment, which agrees with the data available in the literature. A decision to perform neurosurgery for which the patient had indications was made during her first consultation. After regression of discogenic radiculopathy, there was only moderate pain caused by musculoskeletal diseases (facet syndrome, piriformis syndrome that were successfully eliminated by minimally invasive techniques.
Heteroscedasticity checks for regression models
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
For checking on heteroscedasticity in regression models, a unified approach is proposed to constructing test statistics in parametric and nonparametric regression models. For nonparametric regression, the test is not affected sensitively by the choice of smoothing parameters which are involved in estimation of the nonparametric regression function. The limiting null distribution of the test statistic remains the same in a wide range of the smoothing parameters. When the covariate is one-dimensional, the tests are, under some conditions, asymptotically distribution-free. In the high-dimensional cases, the validity of bootstrap approximations is investigated. It is shown that a variant of the wild bootstrap is consistent while the classical bootstrap is not in the general case, but is applicable if some extra assumption on conditional variance of the squared error is imposed. A simulation study is performed to provide evidence of how the tests work and compare with tests that have appeared in the literature. The approach may readily be extended to handle partial linear, and linear autoregressive models.
Cactus: An Introduction to Regression
Hyde, Hartley
2008-01-01
When the author first used "VisiCalc," the author thought it a very useful tool when he had the formulas. But how could he design a spreadsheet if there was no known formula for the quantities he was trying to predict? A few months later, the author relates he learned to use multiple linear regression software and suddenly it all clicked into…
Growth Regression and Economic Theory
Elbers, Chris; Gunning, Jan Willem
2002-01-01
In this note we show that the standard, loglinear growth regression specificationis consistent with one and only one model in the class of stochastic Ramsey models. Thismodel is highly restrictive: it requires a Cobb-Douglas technology and a 100% depreciationrate and it implies that risk does not af
Ridge Regression for Interactive Models.
Tate, Richard L.
1988-01-01
An exploratory study of the value of ridge regression for interactive models is reported. Assuming that the linear terms in a simple interactive model are centered to eliminate non-essential multicollinearity, a variety of common models, representing both ordinal and disordinal interactions, are shown to have "orientations" that are favorable to…
Disease Human - MDC_LowBirthWeight
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...
Physical Activity for a Healthy Weight
... 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 ...
Mapping geogenic radon potential by regression kriging.
Pásztor, László; Szabó, Katalin Zsuzsanna; Szatmári, Gábor; Laborczi, Annamária; Horváth, Ákos
2016-02-15
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. Copyright © 2015 Elsevier B.V. All rights
Logistic regression: a brief primer.
Stoltzfus, Jill C
2011-10-01
Regression techniques are versatile in their application to medical research because they can measure associations, predict outcomes, and control for confounding variable effects. As one such technique, logistic regression is an efficient and powerful way to analyze the effect of a group of independent variables on a binary outcome by quantifying each independent variable's unique contribution. Using components of linear regression reflected in the logit scale, logistic regression iteratively identifies the strongest linear combination of variables with the greatest probability of detecting the observed outcome. Important considerations when conducting logistic regression include selecting independent variables, ensuring that relevant assumptions are met, and choosing an appropriate model building strategy. For independent variable selection, one should be guided by such factors as accepted theory, previous empirical investigations, clinical considerations, and univariate statistical analyses, with acknowledgement of potential confounding variables that should be accounted for. Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers. Additionally, there should be an adequate number of events per independent variable to avoid an overfit model, with commonly recommended minimum "rules of thumb" ranging from 10 to 20 events per covariate. Regarding model building strategies, the three general types are direct/standard, sequential/hierarchical, and stepwise/statistical, with each having a different emphasis and purpose. Before reaching definitive conclusions from the results of any of these methods, one should formally quantify the model's internal validity (i.e., replicability within the same data set) and external validity (i.e., generalizability beyond the current sample). The resulting logistic regression model
Weighted approximation with varying weight
Totik, Vilmos
1994-01-01
A new construction is given for approximating a logarithmic potential by a discrete one. This yields a new approach to approximation with weighted polynomials of the form w"n"(" "= uppercase)P"n"(" "= uppercase). The new technique settles several open problems, and it leads to a simple proof for the strong asymptotics on some L p(uppercase) extremal problems on the real line with exponential weights, which, for the case p=2, are equivalent to power- type asymptotics for the leading coefficients of the corresponding orthogonal polynomials. The method is also modified toyield (in a sense) uniformly good approximation on the whole support. This allows one to deduce strong asymptotics in some L p(uppercase) extremal problems with varying weights. Applications are given, relating to fast decreasing polynomials, asymptotic behavior of orthogonal polynomials and multipoint Pade approximation. The approach is potential-theoretic, but the text is self-contained.
Spectral Experts for Estimating Mixtures of Linear Regressions
Chaganty, Arun Tejasvi; Liang, Percy
2013-01-01
Discriminative latent-variable models are typically learned using EM or gradient-based optimization, which suffer from local optima. In this paper, we develop a new computationally efficient and provably consistent estimator for a mixture of linear regressions, a simple instance of a discriminative latent-variable model. Our approach relies on a low-rank linear regression to recover a symmetric tensor, which can be factorized into the parameters using a tensor power method. We prove rates of ...
Regression model for tuning the PID controller with fractional order time delay system
S.P. Agnihotri; Laxman Madhavrao Waghmare
2014-01-01
In this paper a regression model based for tuning proportional integral derivative (PID) controller with fractional order time delay system is proposed. The novelty of this paper is that tuning parameters of the fractional order time delay system are optimally predicted using the regression model. In the proposed method, the output parameters of the fractional order system are used to derive the regression function. Here, the regression model depends on the weights of the exponential function...
Correlates of Low Birth Weight
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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.
Nieuwenhuijsen, Mark J; Northstone, Kate; Golding, Jean
2002-11-01
Swimmers can be exposed to high levels of trihalomethanes, byproducts of chlorination disinfection. There are no published studies on the relation between swimming and birth weight. We explored this relation in a large birth cohort, the Avon (England) Longitudinal Study of Parents and Children (ALSPAC), in 1991-1992. Information on the amount of swimming per week during the first 18-20 weeks of pregnancy was available for 11,462 pregnant women. Fifty-nine percent never swam, 31% swam up to 1 hour per week, and 10% swam for longer. We used linear regression to explore the relation between birth weight and the amount of swimming, with adjustment for gestational age, maternal age, parity, maternal education level, ethnicity, housing tenure, drug use, smoking and alcohol consumption. We found little effect of the amount of swimming on birth weight. More highly educated women were more likely to swim compared with less educated women, whereas smokers were less likely to swim compared with nonsmokers. There appears to be no relation between the duration of swimming and birth weight.
Regression Verification Using Impact Summaries
Backes, John; Person, Suzette J.; Rungta, Neha; Thachuk, Oksana
2013-01-01
Regression verification techniques are used to prove equivalence of syntactically similar programs. Checking equivalence of large programs, however, can be computationally expensive. Existing regression verification techniques rely on abstraction and decomposition techniques to reduce the computational effort of checking equivalence of the entire program. These techniques are sound but not complete. In this work, we propose a novel approach to improve scalability of regression verification by classifying the program behaviors generated during symbolic execution as either impacted or unimpacted. Our technique uses a combination of static analysis and symbolic execution to generate summaries of impacted program behaviors. The impact summaries are then checked for equivalence using an o-the-shelf decision procedure. We prove that our approach is both sound and complete for sequential programs, with respect to the depth bound of symbolic execution. Our evaluation on a set of sequential C artifacts shows that reducing the size of the summaries can help reduce the cost of software equivalence checking. Various reduction, abstraction, and compositional techniques have been developed to help scale software verification techniques to industrial-sized systems. Although such techniques have greatly increased the size and complexity of systems that can be checked, analysis of large software systems remains costly. Regression analysis techniques, e.g., regression testing [16], regression model checking [22], and regression verification [19], restrict the scope of the analysis by leveraging the differences between program versions. These techniques are based on the idea that if code is checked early in development, then subsequent versions can be checked against a prior (checked) version, leveraging the results of the previous analysis to reduce analysis cost of the current version. Regression verification addresses the problem of proving equivalence of closely related program
Weighted guided image filtering.
Li, Zhengguo; Zheng, Jinghong; Zhu, Zijian; Yao, Wei; Wu, Shiqian
2015-01-01
It is known that local filtering-based edge preserving smoothing techniques suffer from halo artifacts. In this paper, a weighted guided image filter (WGIF) is introduced by incorporating an edge-aware weighting into an existing guided image filter (GIF) to address the problem. The WGIF inherits advantages of both global and local smoothing filters in the sense that: 1) the complexity of the WGIF is O(N) for an image with N pixels, which is same as the GIF and 2) the WGIF can avoid halo artifacts like the existing global smoothing filters. The WGIF is applied for single image detail enhancement, single image haze removal, and fusion of differently exposed images. Experimental results show that the resultant algorithms produce images with better visual quality and at the same time halo artifacts can be reduced/avoided from appearing in the final images with negligible increment on running times.
Bayesian multimodel inference for geostatistical regression models.
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Devin S Johnson
Full Text Available The problem of simultaneous covariate selection and parameter inference for spatial regression models is considered. Previous research has shown that failure to take spatial correlation into account can influence the outcome of standard model selection methods. A Markov chain Monte Carlo (MCMC method is investigated for the calculation of parameter estimates and posterior model probabilities for spatial regression models. The method can accommodate normal and non-normal response data and a large number of covariates. Thus the method is very flexible and can be used to fit spatial linear models, spatial linear mixed models, and spatial generalized linear mixed models (GLMMs. The Bayesian MCMC method also allows a priori unequal weighting of covariates, which is not possible with many model selection methods such as Akaike's information criterion (AIC. The proposed method is demonstrated on two data sets. The first is the whiptail lizard data set which has been previously analyzed by other researchers investigating model selection methods. Our results confirmed the previous analysis suggesting that sandy soil and ant abundance were strongly associated with lizard abundance. The second data set concerned pollution tolerant fish abundance in relation to several environmental factors. Results indicate that abundance is positively related to Strahler stream order and a habitat quality index. Abundance is negatively related to percent watershed disturbance.
Variations in weight stigma concerns
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Jessica E. Cornick
2016-12-01
Full Text Available Over the past 40 years, obesity rates in the United States have grown significantly; these rates have not grown uniformly across the United States (18 of the 20 counties with the highest obesity rates are located in the South. Obesity increases cardiovascular disease risk factors and new research has highlighted the negative psychological effects of obesity, known as weight stigma, including decreased selfcontrol resources, over eating, and exercise avoidance. The primary objective of this study was to determine if weight stigma concerns varied regionally and if social behaviors influenced this variation. In two studies, we collected cross-sectional data from participants in the United States including height and weight, weight stigma concerns, and perception of friends’ preoccupation with weight and dieting. We also collected each participant’s home zip code which was used to locate local obesity rate. We established differences in the relationship between body mass index and weight stigma concerns by local county obesity rate and showed that perceived friend preoccupation with weight and dieting mediated this relationship for individuals in low and medium obesity rate counties. For individuals living in United States counties with lower levels of obesity, increases in personal body mass index leads to increased weight stigma concerns due to an increase in perceived friend preoccupation with weight and dieting. These results indicate that relationships between body mass index, weight stigma concerns, and social networks vary significantly for subpopulations throughout the United States.
Variations in Weight Stigma Concerns
Teter, Cambridge; K.Thaw, Andrew
2016-01-01
Over the past 40 years, obesity rates in the United States have grown significantly; these rates have not grown uniformly across the United States (18 of the 20 counties with the highest obesity rates are located in the South). Obesity increases cardiovascular disease risk factors and new research has highlighted the negative psychological effects of obesity, known as weight stigma, including decreased selfcontrol resources, over eating, and exercise avoidance. The primary objective of this study was to determine if weight stigma concerns varied regionally and if social behaviors influenced this variation. In two studies, we collected cross-sectional data from participants in the United States including height and weight, weight stigma concerns, and perception of friends’ preoccupation with weight and dieting. We also collected each participant’s home zip code which was used to locate local obesity rate. We established differences in the relationship between body mass index and weight stigma concerns by local county obesity rate and showed that perceived friend preoccupation with weight and dieting mediated this relationship for individuals in low and medium obesity rate counties. For individuals living in United States counties with lower levels of obesity, increases in personal body mass index leads to increased weight stigma concerns due to an increase in perceived friend preoccupation with weight and dieting. These results indicate that relationships between body mass index, weight stigma concerns, and social networks vary significantly for subpopulations throughout the United States. PMID:28058288
Are normal-weight adolescents satisfied with their weight?
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Mariana Contiero San Martini
Full Text Available ABSTRACT: CONTEXT AND OBJECTIVE: The high prevalence of obesity has led to public policies for combating it. People with normal weight may gain greater awareness of this issue and change their perceptions of their weight. The aim of this study was to evaluate the prevalence of body weight dissatisfaction among normal-weight adolescents, according to demographic and socioeconomic variables, health-related behavior and morbidities. DESIGN AND SETTING: Population-based cross-sectional study that used data from a health survey conducted in the city of Campinas, São Paulo, in 2008-2009. METHODS: The prevalence and prevalence ratios of weight dissatisfaction were estimated according to independent variables, by means of simple and multiple Poisson regression. RESULTS: 573 normal-weight adolescents aged 10 to 19 years (mean age 14.7 years were analyzed. The prevalence of weight dissatisfaction was 43.7% (95% confidence interval, CI: 37.8-49.8. Higher prevalences of weight dissatisfaction were observed among females, individuals aged 15 to 19 years, those whose households had eight or more domestic appliances, former smokers, individuals who reported alcohol intake and those who had one or more chronic diseases. Lower prevalence of dissatisfaction was observed among adolescents living in substandard housing. Among the normal-weight adolescents, 26.1% wished to lose weight and 17.6% wished to gain weight. CONCLUSION: The results from this study indicate that even when weight is seen to be within the normal range, a high proportion of adolescents express dissatisfaction with their weight, especially females, older adolescents and those of higher socioeconomic level.
Source apportionment advances using polar plots of bivariate correlation and regression statistics
Grange, Stuart K.; Lewis, Alastair C.; Carslaw, David C.
2016-11-01
This paper outlines the development of enhanced bivariate polar plots that allow the concentrations of two pollutants to be compared using pair-wise statistics for exploring the sources of atmospheric pollutants. The new method combines bivariate polar plots, which provide source characteristic information, with pair-wise statistics that provide information on how two pollutants are related to one another. The pair-wise statistics implemented include weighted Pearson correlation and slope from two linear regression methods. The development uses a Gaussian kernel to locally weight the statistical calculations on a wind speed-direction surface together with variable-scaling. Example applications of the enhanced polar plots are presented by using routine air quality data for two monitoring sites in London, United Kingdom for a single year (2013). The London examples demonstrate that the combination of bivariate polar plots, correlation, and regression techniques can offer considerable insight into air pollution source characteristics, which would be missed if only scatter plots and mean polar plots were used for analysis. Specifically, using correlation and slopes as pair-wise statistics, long-range transport processes were isolated and black carbon (BC) contributions to PM2.5 for a kerbside monitoring location were quantified. Wider applications and future advancements are also discussed.
Polynomial Regressions and Nonsense Inference
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Daniel Ventosa-Santaulària
2013-11-01
Full Text Available Polynomial specifications are widely used, not only in applied economics, but also in epidemiology, physics, political analysis and psychology, just to mention a few examples. In many cases, the data employed to estimate such specifications are time series that may exhibit stochastic nonstationary behavior. We extend Phillips’ results (Phillips, P. Understanding spurious regressions in econometrics. J. Econom. 1986, 33, 311–340. by proving that an inference drawn from polynomial specifications, under stochastic nonstationarity, is misleading unless the variables cointegrate. We use a generalized polynomial specification as a vehicle to study its asymptotic and finite-sample properties. Our results, therefore, lead to a call to be cautious whenever practitioners estimate polynomial regressions.
Producing The New Regressive Left
DEFF Research Database (Denmark)
Crone, Christine
to be a committed artist, and how that translates into supporting al-Assad’s rule in Syria; the Ramadan programme Harrir Aqlak’s attempt to relaunch an intellectual renaissance and to promote religious pluralism; and finally, al-Mayadeen’s cooperation with the pan-Latin American TV station TeleSur and its ambitions...... becomes clear from the analytical chapters is the emergence of the new cross-ideological alliance of The New Regressive Left. This emerging coalition between Shia Muslims, religious minorities, parts of the Arab Left, secular cultural producers, and the remnants of the political,strategic resistance...... coalition (Iran, Hizbollah, Syria), capitalises on a series of factors that bring them together in spite of their otherwise diverse worldviews and agendas. The New Regressive Left is united by resistance against the growing influence of Saudi Arabia in the religious, cultural, political, economic...
Quantile Regression With Measurement Error
Wei, Ying
2009-08-27
Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. © 2009 American Statistical Association.
Clustered regression with unknown clusters
Barman, Kishor
2011-01-01
We consider a collection of prediction experiments, which are clustered in the sense that groups of experiments ex- hibit similar relationship between the predictor and response variables. The experiment clusters as well as the regres- sion relationships are unknown. The regression relation- ships define the experiment clusters, and in general, the predictor and response variables may not exhibit any clus- tering. We call this prediction problem clustered regres- sion with unknown clusters (CRUC) and in this paper we focus on linear regression. We study and compare several methods for CRUC, demonstrate their applicability to the Yahoo Learning-to-rank Challenge (YLRC) dataset, and in- vestigate an associated mathematical model. CRUC is at the crossroads of many prior works and we study several prediction algorithms with diverse origins: an adaptation of the expectation-maximization algorithm, an approach in- spired by K-means clustering, the singular value threshold- ing approach to matrix rank minimization u...
Robust nonlinear regression in applications
Lim, Changwon; Sen, Pranab K.; Peddada, Shyamal D.
2013-01-01
Robust statistical methods, such as M-estimators, are needed for nonlinear regression models because of the presence of outliers/influential observations and heteroscedasticity. Outliers and influential observations are commonly observed in many applications, especially in toxicology and agricultural experiments. For example, dose response studies, which are routinely conducted in toxicology and agriculture, sometimes result in potential outliers, especially in the high dose gr...
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.
Astronomical Methods for Nonparametric Regression
Steinhardt, Charles L.; Jermyn, Adam
2017-01-01
I will discuss commonly used techniques for nonparametric regression in astronomy. We find that several of them, particularly running averages and running medians, are generically biased, asymmetric between dependent and independent variables, and perform poorly in recovering the underlying function, even when errors are present only in one variable. We then examine less-commonly used techniques such as Multivariate Adaptive Regressive Splines and Boosted Trees and find them superior in bias, asymmetry, and variance both theoretically and in practice under a wide range of numerical benchmarks. In this context the chief advantage of the common techniques is runtime, which even for large datasets is now measured in microseconds compared with milliseconds for the more statistically robust techniques. This points to a tradeoff between bias, variance, and computational resources which in recent years has shifted heavily in favor of the more advanced methods, primarily driven by Moore's Law. Along these lines, we also propose a new algorithm which has better overall statistical properties than all techniques examined thus far, at the cost of significantly worse runtime, in addition to providing guidance on choosing the nonparametric regression technique most suitable to any specific problem. We then examine the more general problem of errors in both variables and provide a new algorithm which performs well in most cases and lacks the clear asymmetry of existing non-parametric methods, which fail to account for errors in both variables.
Inferring gene regression networks with model trees
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Aguilar-Ruiz Jesus S
2010-10-01
Full Text Available Abstract Background Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. To infer gene regulatory networks, the first step is to find direct regulatory relationships between genes building the so-called gene co-expression networks. They are typically generated using correlation statistics as pairwise similarity measures. Correlation-based methods are very useful in order to determine whether two genes have a strong global similarity but do not detect local similarities. Results We propose model trees as a method to identify gene interaction networks. While correlation-based methods analyze each pair of genes, in our approach we generate a single regression tree for each gene from the remaining genes. Finally, a graph from all the relationships among output and input genes is built taking into account whether the pair of genes is statistically significant. For this reason we apply a statistical procedure to control the false discovery rate. The performance of our approach, named REGNET, is experimentally tested on two well-known data sets: Saccharomyces Cerevisiae and E.coli data set. First, the biological coherence of the results are tested. Second the E.coli transcriptional network (in the Regulon database is used as control to compare the results to that of a correlation-based method. This experiment shows that REGNET performs more accurately at detecting true gene associations than the Pearson and Spearman zeroth and first-order correlation-based methods. Conclusions REGNET generates gene association networks from gene expression data, and differs from correlation-based methods in that the relationship between one gene and others is calculated simultaneously. Model trees are very useful techniques to estimate the numerical values for the target genes by linear regression functions. They are very often more precise than linear regression models because they can add just different linear
Genetics Home Reference: caudal regression syndrome
... Twitter Home Health Conditions caudal regression syndrome caudal regression syndrome Enable Javascript to view the expand/collapse ... Download PDF Open All Close All Description Caudal regression syndrome is a disorder that impairs the development ...
Estimating the Impact of Urbanization on Air Quality in China Using Spatial Regression Models
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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.
Nonparametric additive regression for repeatedly measured data
Carroll, R. J.
2009-05-20
We develop an easily computed smooth backfitting algorithm for additive model fitting in repeated measures problems. Our methodology easily copes with various settings, such as when some covariates are the same over repeated response measurements. We allow for a working covariance matrix for the regression errors, showing that our method is most efficient when the correct covariance matrix is used. The component functions achieve the known asymptotic variance lower bound for the scalar argument case. Smooth backfitting also leads directly to design-independent biases in the local linear case. Simulations show our estimator has smaller variance than the usual kernel estimator. This is also illustrated by an example from nutritional epidemiology. © 2009 Biometrika Trust.
Congenital osteofibrous dysplasia Campanacci: spontaneous postbioptic regression.
Jobke, Björn; Bohndorf, Klaus; Vieth, Volker; Werner, Mathias
2014-04-01
Osteofibrous dysplasia Campanacci is a rare benign bone tumor most frequently observed in young childhood. The exclusive localization in the tibia is very characteristic. The incidence of congenital primary bone tumors is an absolute rarity. We report a case of a newborn with a histologically proven osteofibrous dysplasia Campanacci at the tibia presenting a regular radiographic follow-up. After a small open biopsy and spontaneous minor fracture, the lesion rapidly remodeled within 1½ months and almost completely regressed with restutio ad integrum. Surgical intervention in this tumor entity at childhood age has been shown to have a high recurrence rate but due to lack of experience with newborns, guidelines do not exist. We analyze the radiologic and histologic differential diagnosis of juvenile adamantinoma and emphasize that congenital peripheral bone tumors should be treated conservatively when malignancy is excluded.
Multiatlas segmentation as nonparametric regression.
Awate, Suyash P; Whitaker, Ross T
2014-09-01
This paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation. We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator. We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems.
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.
Stochastic expansions using continuous dictionaries: L\\'{e}vy adaptive regression kernels
Wolpert, Robert L; Tu, Chong; 10.1214/11-AOS889
2011-01-01
This article describes a new class of prior distributions for nonparametric function estimation. The unknown function is modeled as a limit of weighted sums of kernels or generator functions indexed by continuous parameters that control local and global features such as their translation, dilation, modulation and shape. L\\'{e}vy random fields and their stochastic integrals are employed to induce prior distributions for the unknown functions or, equivalently, for the number of kernels and for the parameters governing their features. Scaling, shape, and other features of the generating functions are location-specific to allow quite different function properties in different parts of the space, as with wavelet bases and other methods employing overcomplete dictionaries. We provide conditions under which the stochastic expansions converge in specified Besov or Sobolev norms. Under a Gaussian error model, this may be viewed as a sparse regression problem, with regularization induced via the L\\'{e}vy random field p...
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.
Detecting ecological breakpoints: a new tool for piecewise regression
Directory of Open Access Journals (Sweden)
Alessandro Ferrarini
2011-06-01
Full Text Available Simple linear regression tries to determine a linear relationship between a given variable X (predictor and a dependent variable Y. Since most of the environmental problems involve complex relationships, X-Y relationship is often better modeled through a regression where, instead of fitting a single straight line to the data, the algorithm allows the fitting to bend. Piecewise regressions just do it, since they allow emphasize local, instead of global, rules connecting predictor and dependent variables. In this work, a tool called RolReg is proposed as an implementation of Krummel's method to detect breakpoints in regression models. RolReg, which is freely available upon request from the author, could useful to detect proper breakpoints in ecological laws.
Effects of Predictor Weighting Methods on Incremental Validity.
Sackett, Paul R; Dahlke, Jeffrey A; Shewach, Oren R; Kuncel, Nathan R
2017-05-22
It is common to add an additional predictor to a selection system with the goal of increasing criterion-related validity. Research on the incremental validity of a second predictor is generally based on forming a regression-weighted composite of the predictors. However, in practice predictors are commonly used in ways other than regression-weighted composites, and we examine the robustness of incremental validity findings to other ways of using predictors, namely, unit weighting and multiple hurdles. We show that there are settings in which the incremental value of a second predictor disappears, and can even produce lower validity than the first predictor alone, when these alternatives to regression weighting are used. First, we examine conditions under which unit weighting will negate gain in predictive power attainable via regression weights. Second, we revisit Schmidt and Hunter's (1998) summary of incremental validity of predictors over cognitive ability, evaluating whether the reported incremental value of a second predictor is different when predictors are unit weighted rather than regression weighted. Third, we analyze data reported in the published literature to discern the frequency with which unit weighting might affect conclusions about whether there is value in adding a second predictor to a first. Finally, we shift from unit weighting to multiple hurdle selection, examining conditions under which conclusions about incremental validity differ when regression weighting is replaced by multiple-hurdle selection. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
Prediction, Regression and Critical Realism
DEFF Research Database (Denmark)
Næss, Petter
2004-01-01
This paper considers the possibility of prediction in land use planning, and the use of statistical research methods in analyses of relationships between urban form and travel behaviour. Influential writers within the tradition of critical realism reject the possibility of predicting social...... of prediction necessary and possible in spatial planning of urban development. Finally, the political implications of positions within theory of science rejecting the possibility of predictions about social phenomena are addressed....... phenomena. This position is fundamentally problematic to public planning. Without at least some ability to predict the likely consequences of different proposals, the justification for public sector intervention into market mechanisms will be frail. Statistical methods like regression analyses are commonly...
Nonparametric Regression with Common Shocks
Directory of Open Access Journals (Sweden)
Eduardo A. Souza-Rodrigues
2016-09-01
Full Text Available This paper considers a nonparametric regression model for cross-sectional data in the presence of common shocks. Common shocks are allowed to be very general in nature; they do not need to be finite dimensional with a known (small number of factors. I investigate the properties of the Nadaraya-Watson kernel estimator and determine how general the common shocks can be while still obtaining meaningful kernel estimates. Restrictions on the common shocks are necessary because kernel estimators typically manipulate conditional densities, and conditional densities do not necessarily exist in the present case. By appealing to disintegration theory, I provide sufficient conditions for the existence of such conditional densities and show that the estimator converges in probability to the Kolmogorov conditional expectation given the sigma-field generated by the common shocks. I also establish the rate of convergence and the asymptotic distribution of the kernel estimator.
Practical Session: Multiple Linear Regression
Clausel, M.; Grégoire, G.
2014-12-01
Three exercises are proposed to illustrate the simple linear regression. In the first one investigates the influence of several factors on atmospheric pollution. It has been proposed by D. Chessel and A.B. Dufour in Lyon 1 (see Sect. 6 of http://pbil.univ-lyon1.fr/R/pdf/tdr33.pdf) and is based on data coming from 20 cities of U.S. Exercise 2 is an introduction to model selection whereas Exercise 3 provides a first example of analysis of variance. Exercises 2 and 3 have been proposed by A. Dalalyan at ENPC (see Exercises 2 and 3 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_5.pdf).
Lumbar herniated disc: spontaneous regression
Yüksel, Kasım Zafer
2017-01-01
Background Low back pain is a frequent condition that results in substantial disability and causes admission of patients to neurosurgery clinics. To evaluate and present the therapeutic outcomes in lumbar disc hernia (LDH) patients treated by means of a conservative approach, consisting of bed rest and medical therapy. Methods This retrospective cohort was carried out in the neurosurgery departments of hospitals in Kahramanmaraş city and 23 patients diagnosed with LDH at the levels of L3−L4, L4−L5 or L5−S1 were enrolled. Results The average age was 38.4 ± 8.0 and the chief complaint was low back pain and sciatica radiating to one or both lower extremities. Conservative treatment was administered. Neurological examination findings, durations of treatment and intervals until symptomatic recovery were recorded. Laségue tests and neurosensory examination revealed that mild neurological deficits existed in 16 of our patients. Previously, 5 patients had received physiotherapy and 7 patients had been on medical treatment. The number of patients with LDH at the level of L3−L4, L4−L5, and L5−S1 were 1, 13, and 9, respectively. All patients reported that they had benefit from medical treatment and bed rest, and radiologic improvement was observed simultaneously on MRI scans. The average duration until symptomatic recovery and/or regression of LDH symptoms was 13.6 ± 5.4 months (range: 5−22). Conclusions It should be kept in mind that lumbar disc hernias could regress with medical treatment and rest without surgery, and there should be an awareness that these patients could recover radiologically. This condition must be taken into account during decision making for surgical intervention in LDH patients devoid of indications for emergent surgery. PMID:28119770
Credit Scoring Problem Based on Regression Analysis
Khassawneh, Bashar Suhil Jad Allah
2014-01-01
ABSTRACT: This thesis provides an explanatory introduction to the regression models of data mining and contains basic definitions of key terms in the linear, multiple and logistic regression models. Meanwhile, the aim of this study is to illustrate fitting models for the credit scoring problem using simple linear, multiple linear and logistic regression models and also to analyze the found model functions by statistical tools. Keywords: Data mining, linear regression, logistic regression....
Importance of early weight change in a pediatric weight management trial.
Goldschmidt, Andrea B; Stein, Richard I; Saelens, Brian E; Theim, Kelly R; Epstein, Leonard H; Wilfley, Denise E
2011-07-01
Early weight change is associated with overall weight loss treatment response in adults but has been relatively unexplored in youth. We investigated the importance of early weight change in a pediatric weight control trial. Overweight children aged 7 to 12 years (n=204) participated in a randomized controlled trial of 2 weight maintenance treatments (MTs) after a 20-week family-based behavioral weight loss treatment (FBT). Hierarchical regression was used to investigate the relation between children's percentage weight change at sessions 4, 6, and 8 of FBT and BMI z-score reductions after FBT and at the 2-year follow-up. Correlations and hierarchical regression were used to identify child and parent factors associated with children's early weight change. Children's percentage weight change by FBT session 8 was the best predictor of BMI z-score reductions after FBT and at 2-year follow-up. Percentage weight change in children at session 8 was associated with better FBT attendance and with greater percentage weight change in parents at FBT session 8. Early weight change seems to be related to treatment response through the end of treatment and 2-year follow-up. Future research should include investigation of strategies to promote early weight change in children and parents and identification of mechanisms through which early weight change is related to overall treatment response. Copyright © 2011 by the American Academy of Pediatrics.
Institute of Scientific and Technical Information of China (English)
沈笑慧; 张健; 何熊熊
2012-01-01
基于接收信号强度指示的无线传感器网络定位问题,提出一种改进Kalman滤波方法,消除测距过程中的非视距误差,得到标签与节点间的估计距离.然后,分析标签与节点的距离、定位单元质量和标签所处的位置三方面对定位精度的影响,提出一种改进三边定位算法,并根据滤波后的估计距离计算得到的多个定位坐标进行加权融合.最后,通过Matlab仿真验证所提算法的有效性.%Based on the received signal strength indicator wireless sensor network location problem, the article puts forward an improved Kalman filtering method to obtain the evaluated distance between label and nodes through eliminating the non-line-of-sight error in the ranging process. Then, three effects on location accuracy are analysed, which are the distance between label and node, the quality of location unit and the position of label and an improved trilateral localization algorithm is proposed, in which weighted fusion of some position coordinates calculated by the filtered distance. Finally, the simulation is given to demonstrate the effectiveness of the proposed algorithm.
Central limit theorem of linear regression model under right censorship
Institute of Scientific and Technical Information of China (English)
HE; Shuyuan(何书元); HUANG; Xiang(Heung; Wong)(黄香)
2003-01-01
In this paper, the estimation of joint distribution F(y,z) of (Y, Z) and the estimation in thelinear regression model Y = b′Z + ε for complete data are extended to that of the right censored data. Theregression parameter estimates of b and the variance of ε are weighted least square estimates with randomweights. The central limit theorems of the estimators are obtained under very weak conditions and the derivedasymptotic variance has a very simple form.
Prediction of siRNA potency using sparse logistic regression.
Hu, Wei; Hu, John
2014-06-01
RNA interference (RNAi) can modulate gene expression at post-transcriptional as well as transcriptional levels. Short interfering RNA (siRNA) serves as a trigger for the RNAi gene inhibition mechanism, and therefore is a crucial intermediate step in RNAi. There have been extensive studies to identify the sequence characteristics of potent siRNAs. One such study built a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) to measure the contribution of each siRNA sequence feature. This model is simple and interpretable, but it requires a large number of nonzero weights. We have introduced a novel technique, sparse logistic regression, to build a linear model using single-position specific nucleotide compositions which has the same prediction accuracy of the linear model based on LASSO. The weights in our new model share the same general trend as those in the previous model, but have only 25 nonzero weights out of a total 84 weights, a 54% reduction compared to the previous model. Contrary to the linear model based on LASSO, our model suggests that only a few positions are influential on the efficacy of the siRNA, which are the 5' and 3' ends and the seed region of siRNA sequences. We also employed sparse logistic regression to build a linear model using dual-position specific nucleotide compositions, a task LASSO is not able to accomplish well due to its high dimensional nature. Our results demonstrate the superiority of sparse logistic regression as a technique for both feature selection and regression over LASSO in the context of siRNA design.
Varying-coefficient functional linear regression
Wu, Yichao; Müller, Hans-Georg; 10.3150/09-BEJ231
2011-01-01
Functional linear regression analysis aims to model regression relations which include a functional predictor. The analog of the regression parameter vector or matrix in conventional multivariate or multiple-response linear regression models is a regression parameter function in one or two arguments. If, in addition, one has scalar predictors, as is often the case in applications to longitudinal studies, the question arises how to incorporate these into a functional regression model. We study a varying-coefficient approach where the scalar covariates are modeled as additional arguments of the regression parameter function. This extension of the functional linear regression model is analogous to the extension of conventional linear regression models to varying-coefficient models and shares its advantages, such as increased flexibility; however, the details of this extension are more challenging in the functional case. Our methodology combines smoothing methods with regularization by truncation at a finite numb...
Functional Regression for Quasar Spectra
Ciollaro, Mattia; Freeman, Peter; Genovese, Christopher; Lei, Jing; O'Connell, Ross; Wasserman, Larry
2014-01-01
The Lyman-alpha forest is a portion of the observed light spectrum of distant galactic nuclei which allows us to probe remote regions of the Universe that are otherwise inaccessible. The observed Lyman-alpha forest of a quasar light spectrum can be modeled as a noisy realization of a smooth curve that is affected by a `damping effect' which occurs whenever the light emitted by the quasar travels through regions of the Universe with higher matter concentration. To decode the information conveyed by the Lyman-alpha forest about the matter distribution, we must be able to separate the smooth `continuum' from the noise and the contribution of the damping effect in the quasar light spectra. To predict the continuum in the Lyman-alpha forest, we use a nonparametric functional regression model in which both the response and the predictor variable (the smooth part of the damping-free portion of the spectrum) are function-valued random variables. We demonstrate that the proposed method accurately predicts the unobserv...
Knowledge and Awareness: Linear Regression
Directory of Open Access Journals (Sweden)
Monika Raghuvanshi
2016-12-01
Full Text Available Knowledge and awareness are factors guiding development of an individual. These may seem simple and practicable, but in reality a proper combination of these is a complex task. Economically driven state of development in younger generations is an impediment to the correct manner of development. As youths are at the learning phase, they can be molded to follow a correct lifestyle. Awareness and knowledge are important components of any formal or informal environmental education. The purpose of this study is to evaluate the relationship of these components among students of secondary/ senior secondary schools who have undergone a formal study of environment in their curricula. A suitable instrument is developed in order to measure the elements of Awareness and Knowledge among the participants of the study. Data was collected from various secondary and senior secondary school students in the age group 14 to 20 years using cluster sampling technique from the city of Bikaner, India. Linear regression analysis was performed using IBM SPSS 23 statistical tool. There exists a weak relation between knowledge and awareness about environmental issues, caused due to routine practices mishandling; hence one component can be complemented by other for improvement in both. Knowledge and awareness are crucial factors and can provide huge opportunities in any field. Resource utilization for economic solutions may pave the way for eco-friendly products and practices. If green practices are inculcated at the learning phase, they may become normal routine. This will also help in repletion of the environment.
Streamflow forecasting using functional regression
Masselot, Pierre; Dabo-Niang, Sophie; Chebana, Fateh; Ouarda, Taha B. M. J.
2016-07-01
Streamflow, as a natural phenomenon, is continuous in time and so are the meteorological variables which influence its variability. In practice, it can be of interest to forecast the whole flow curve instead of points (daily or hourly). To this end, this paper introduces the functional linear models and adapts it to hydrological forecasting. More precisely, functional linear models are regression models based on curves instead of single values. They allow to consider the whole process instead of a limited number of time points or features. We apply these models to analyse the flow volume and the whole streamflow curve during a given period by using precipitations curves. The functional model is shown to lead to encouraging results. The potential of functional linear models to detect special features that would have been hard to see otherwise is pointed out. The functional model is also compared to the artificial neural network approach and the advantages and disadvantages of both models are discussed. Finally, future research directions involving the functional model in hydrology are presented.
Principal component regression analysis with SPSS.
Liu, R X; Kuang, J; Gong, Q; Hou, X L
2003-06-01
The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.
A comparison of various methods for multivariate regression with highly collinear variables
Kiers, Henk A.L.; Smilde, Age K.
2007-01-01
Regression tends to give very unstable and unreliable regression weights when predictors are highly collinear. Several methods have been proposed to counter this problem. A subset of these do so by finding components that summarize the information in the predictors and the criterion variables. The p
A comparison of various methods for multivariate regression with highly collinear variables
Kiers, Henk A.L.; Smilde, Age K.
2007-01-01
Regression tends to give very unstable and unreliable regression weights when predictors are highly collinear. Several methods have been proposed to counter this problem. A subset of these do so by finding components that summarize the information in the predictors and the criterion variables. The p
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.
Analysis of Birth weight using Singular Value Decomposition
Nagarajan, D; Nagarajan, V; Seethalekshmi, V
2010-01-01
The researchers have drawn much attention about the birth weight of newborn babies in the last three decades. The birth weight is one of the vital roles in the babys health. So many researchers such as (2),(1) and (4) analyzed the birth weight of babies. The aim of this paper is to analyze the birth weight and some other birth weight related variable, using singular value decomposition and multiple linear regression.
Effects of vacation properties on local education budgets
Directory of Open Access Journals (Sweden)
Jason Giersch
2014-12-01
Full Text Available Residents of school districts with large percentages of vacation properties have the opportunity to export a portion of their school taxes onto the owners of those vacation properties. Those property owners are unlikely to consume educational services or have the opportunity to vote against local school taxes. Previous studies address exportation of taxes onto vacation property owners and the effects on local government budgets generally but not on education finances specifically. This study connects research on rates of vacation properties with that on local education finances by using data from the state of Georgia in 2010 and weighted least squares regression analysis to show that high percentages of vacation properties do indeed result in larger local school expenditures.
Spontaneous Regression of an Incidental Spinal Meningioma
National Research Council Canada - National Science Library
Yilmaz, Ali; Kizilay, Zahir; Sair, Ahmet; Avcil, Mucahit; Ozkul, Ayca
2015-01-01
AIM: The regression of meningioma has been reported in literature before. In spite of the fact that the regression may be involved by hemorrhage, calcification or some drugs withdrawal, it is rarely observed spontaneously. CASE REPORT...
Common pitfalls in statistical analysis: Logistic regression.
Ranganathan, Priya; Pramesh, C S; Aggarwal, Rakesh
2017-01-01
Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.
Investigating DRG cost weights for hospitals in middle income countries.
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.
Vanderbei, Robert J
2012-01-01
Using 55 years of daily average temperatures from a local weather station, I made a least-absolute-deviations (LAD) regression model that accounts for three effects: seasonal variations, the 11-year solar cycle, and a linear trend. The model was formulated as a linear programming problem and solved using widely available optimization software. The solution indicates that temperatures have gone up by about 2 degrees Fahrenheit over the 55 years covered by the data. It also correctly identifies the known phase of the solar cycle; i.e., the date of the last solar minimum. It turns out that the maximum slope of the solar cycle sinusoid in the regression model is about the same size as the slope produced by the linear trend. The fact that the solar cycle was correctly extracted by the model is a strong indicator that effects of this size, in particular the slope of the linear trend, can be accurately determined from the 55 years of data analyzed. The main purpose for doing this analysis is to demonstrate that it i...
Semiparametric regression during 2003–2007
Ruppert, David
2009-01-01
Semiparametric regression is a fusion between parametric regression and nonparametric regression that integrates low-rank penalized splines, mixed model and hierarchical Bayesian methodology – thus allowing more streamlined handling of longitudinal and spatial correlation. We review progress in the field over the five-year period between 2003 and 2007. We find semiparametric regression to be a vibrant field with substantial involvement and activity, continual enhancement and widespread application.
Unbalanced Regressions and the Predictive Equation
DEFF Research Database (Denmark)
Osterrieder, Daniela; Ventosa-Santaulària, Daniel; Vera-Valdés, J. Eduardo
Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness in the theoreti......Predictive return regressions with persistent regressors are typically plagued by (asymptotically) biased/inconsistent estimates of the slope, non-standard or potentially even spurious statistical inference, and regression unbalancedness. We alleviate the problem of unbalancedness...
The use of GLS regression in regional hydrologic analyses
Griffis, V. W.; Stedinger, J. R.
2007-09-01
SummaryTo estimate flood quantiles and other statistics at ungauged sites, many organizations employ an iterative generalized least squares (GLS) regression procedure to estimate the parameters of a model of the statistic of interest as a function of basin characteristics. The GLS regression procedure accounts for differences in available record lengths and spatial correlation in concurrent events by using an estimator of the sampling covariance matrix of available flood quantiles. Previous studies by the US Geological Survey using the LP3 distribution have neglected the impact of uncertainty in the weighted skew on quantile precision. The needed relationship is developed here and its use is illustrated in a regional flood study with 162 sites from South Carolina. The performance of a pooled regression model is compared to separate models for each hydrologic region: statistical tests recommend an interesting hybrid of the two which is both surprising and hydrologically reasonable. The statistical analysis is augmented with new diagnostic metrics including a condition number to check for multicollinearity, a new pseudo- R appropriate for use with GLS regression, and two error variance ratios. GLS regression for the standard deviation demonstrates that again a hybrid model is attractive, and that GLS rather than an OLS or WLS analysis is appropriate for the development of regional standard deviation models.
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
Synthesizing Regression Results: A Factored Likelihood Method
Wu, Meng-Jia; Becker, Betsy Jane
2013-01-01
Regression methods are widely used by researchers in many fields, yet methods for synthesizing regression results are scarce. This study proposes using a factored likelihood method, originally developed to handle missing data, to appropriately synthesize regression models involving different predictors. This method uses the correlations reported…
Regression Analysis by Example. 5th Edition
Chatterjee, Samprit; Hadi, Ali S.
2012-01-01
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 and thoroughly…
Standards for Standardized Logistic Regression Coefficients
Menard, Scott
2011-01-01
Standardized coefficients in logistic regression analysis have the same utility as standardized coefficients in linear regression analysis. Although there has been no consensus on the best way to construct standardized logistic regression coefficients, there is now sufficient evidence to suggest a single best approach to the construction of a…
Locally supervised neural networks for approximating terramechanics models
Song, Xingguo; Gao, Haibo; Ding, Liang; Spanos, Pol D.; Deng, Zongquan; Li, Zhijun
2016-06-01
Neural networks (NNs) have been widely implemented for identifying nonlinear models, and predicting the distribution of targets, due to their ability to store and learn training samples. However, for highly complex systems, it is difficult to build a robust global network model, and efficiently managing the large amounts of experimental data is often required in real-time applications. In this paper, an effective method for building local models is proposed to enhance robustness and learning speed in globally supervised NNs. Unlike NNs, Gaussian processes (GP) produce predictions that capture the uncertainty inherent in actual systems, and typically provides superior results. Therefore, in this study, each local NN is learned in the same manner as a Gaussian process. A mixture of local model NNs is created and then augmented using weighted regression. This proposed method, referred to as locally supervised NN for weighted regression like GP, is abbreviated as "LGPN", is utilized for approximating a wheel-terrain interaction model under fixed soil parameters. The prediction results show that the proposed method yields significant robustness, modeling accuracy, and rapid learning speed.
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 Breastfeeding was negatively associated with PPWR in all women but those...
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.
Testing the gonadal regression-cytoprotection hypothesis.
Crawford, B A; Spaliviero, J A; Simpson, J M; Handelsman, D J
1998-11-15
Germinal damage is an almost universal accompaniment of cancer treatment as the result of bystander damage to the testis from cytotoxic drugs and/or irradiation. Cancer treatment for the most common cancers of the reproductive age group in men has improved such that most are now treated with curative intent, and many others are treated with likelihood of prolonged survival, so that the preservation of fertility is an important component of posttreatment quality of life. This has led to the consideration of developing adjuvant treatments that may reduce the gonadal toxicity of cancer therapy. One dominant hypothesis has been based on the supposition that the immature testis was resistant to cytotoxin damage. Hence, if hormonal treatment were able to cause spermatogenic regression to an immature state via an effective withdrawal of gonadotrophin secretion, the testis might be maintained temporarily in a protected state during cytotoxin exposure. However, clinical studies have been disappointing but have also been unable to test the hypothesis definitively thus far, due to the inability to completely suppress gonadotrophin secretion. Similarly, experimental models have also given conflicting results and, at best, a modest cytoprotection. To definitively test this hypothesis experimentally, we used the fact that the functionally hpg mouse has complete gonadotrophin deficiency but can undergo the induction of full spermatogenesis by testosterone. Thus, if complete gonadotrophin deficiency were an advantage during cytotoxin exposure, then the hpg mouse should exhibit some degree of germinal protection against cytotoxin-induced damage. We therefore administered three different cytotoxins (200 mg/kg procarbazine, 9 mg/kg doxorubicin, 8 Gy of X irradiation) to produce a range of severity in testicular damage and mechanism of action to either phenotypically normal or hpg mice. Testis weight and homogenization-resistant spermatid numbers were measured to evaluate the
Spatial vulnerability assessments by regression kriging
Pásztor, László; Laborczi, Annamária; Takács, Katalin; Szatmári, Gábor
2016-04-01
information representing IEW or GRP forming environmental factors were taken into account to support the spatial inference of the locally experienced IEW frequency and measured GRP values respectively. An efficient spatial prediction methodology was applied to construct reliable maps, namely regression kriging (RK) 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. Application of RK also provides the possibility of inherent accuracy assessment. The resulting maps are characterized by global and local measures of its accuracy. Additionally the method enables interval estimation for spatial extension of the areas of predefined risk categories. All of these outputs provide useful contribution to spatial planning, action planning and decision making. Acknowledgement: Our work was partly supported by the Hungarian National Scientific Research Foundation (OTKA, Grant No. K105167).
Institute of Scientific and Technical Information of China (English)
LU; Zudi
2001-01-01
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Assumptions of Multiple Regression: Correcting Two Misconceptions
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Matt N. Williams
2013-09-01
Full Text Available In 2002, an article entitled - Four assumptions of multiple regression that researchers should always test- by.Osborne and Waters was published in PARE. This article has gone on to be viewed more than 275,000 times.(as of August 2013, and it is one of the first results displayed in a Google search for - regression.assumptions- . While Osborne and Waters' efforts in raising awareness of the need to check assumptions.when using regression are laudable, we note that the original article contained at least two fairly important.misconceptions about the assumptions of multiple regression: Firstly, that multiple regression requires the.assumption of normally distributed variables; and secondly, that measurement errors necessarily cause.underestimation of simple regression coefficients. In this article, we clarify that multiple regression models.estimated using ordinary least squares require the assumption of normally distributed errors in order for.trustworthy inferences, at least in small samples, but not the assumption of normally distributed response or.predictor variables. Secondly, we point out that regression coefficients in simple regression models will be.biased (toward zero estimates of the relationships between variables of interest when measurement error is.uncorrelated across those variables, but that when correlated measurement error is present, regression.coefficients may be either upwardly or downwardly biased. We conclude with a brief corrected summary of.the assumptions of multiple regression when using ordinary least squares.
Functional linear regression via canonical analysis
He, Guozhong; Wang, Jane-Ling; Yang, Wenjing; 10.3150/09-BEJ228
2011-01-01
We study regression models for the situation where both dependent and independent variables are square-integrable stochastic processes. Questions concerning the definition and existence of the corresponding functional linear regression models and some basic properties are explored for this situation. We derive a representation of the regression parameter function in terms of the canonical components of the processes involved. This representation establishes a connection between functional regression and functional canonical analysis and suggests alternative approaches for the implementation of functional linear regression analysis. A specific procedure for the estimation of the regression parameter function using canonical expansions is proposed and compared with an established functional principal component regression approach. As an example of an application, we present an analysis of mortality data for cohorts of medflies, obtained in experimental studies of aging and longevity.
Tools to support interpreting multiple regression in the face of multicollinearity.
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.
Modeling personalized head-related impulse response using support vector regression
Institute of Scientific and Technical Information of China (English)
HUANG Qing-hua; FANG Yong
2009-01-01
A new customization approach based on support vector regression (SVR) is proposed to obtain individual headrelated impulse response (HRIR) without complex measurement and special equipment. Principal component analysis (PCA) is first applied to obtain a few principal components and corresponding weight vectors correlated with individual anthropometric parameters. Then the weight vectors act as output of the nonlinear regression model. Some measured anthropometric parameters are selected as input of the model according to the correlation coefficients between the parameters and the weight vectors. After the regression model is learned from the training data, the individual HRIR can be predicted based on the measured anthropometric parameters. Compared with a back-propagation neural network (BPNN) for nonlinear regression,better generalization and prediction performance for small training samples can be obtained using the proposed PCA-SVR algorithm.
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Mustafa Koroglu
2016-02-01
Full Text Available This paper considers a functional-coefficient spatial Durbin model with nonparametric spatial weights. Applying the series approximation method, we estimate the unknown functional coefficients and spatial weighting functions via a nonparametric two-stage least squares (or 2SLS estimation method. To further improve estimation accuracy, we also construct a second-step estimator of the unknown functional coefficients by a local linear regression approach. Some Monte Carlo simulation results are reported to assess the finite sample performance of our proposed estimators. We then apply the proposed model to re-examine national economic growth by augmenting the conventional Solow economic growth convergence model with unknown spatial interactive structures of the national economy, as well as country-specific Solow parameters, where the spatial weighting functions and Solow parameters are allowed to be a function of geographical distance and the countries’ openness to trade, respectively.
Regression in children with autism spectrum disorders.
Malhi, Prahbhjot; Singhi, Pratibha
2012-10-01
To understand the characteristics of autistic regression and to compare the clinical and developmental profile of children with autism spectrum disorders (ASD) in whom parents report developmental regression with age matched ASD children in whom no regression is reported. Participants were 35 (Mean age = 3.57 y, SD = 1.09) children with ASD in whom parents reported developmental regression before age 3 y and a group of age and IQ matched 35 ASD children in whom parents did not report regression. All children were recruited from the outpatient Child Psychology Clinic of the Department of Pediatrics of a tertiary care teaching hospital in North India. Multi-disciplinary evaluations including neurological, diagnostic, cognitive, and behavioral assessments were done. Parents were asked in detail about the age at onset of regression, type of regression, milestones lost, and event, if any, related to the regression. In addition, the Childhood Autism Rating Scale (CARS) was administered to assess symptom severity. The mean age at regression was 22.43 mo (SD = 6.57) and large majority (66.7%) of the parents reported regression between 12 and 24 mo. Most (75%) of the parents of the regression-autistic group reported regression in the language domain, particularly in the expressive language sector, usually between 18 and 24 mo of age. Regression of language was not an isolated phenomenon and regression in other domains was also reported including social skills (75%), cognition (31.25%). In majority of the cases (75%) the regression reported was slow and subtle. There were no significant differences in the motor, social, self help, and communication functioning between the two groups as measured by the DP II.There were also no significant differences between the two groups on the total CARS score and total number of DSM IV symptoms endorsed. However, the regressed children had significantly (t = 2.36, P = .021) more social deficits as per the DSM IV as
The allometry of coarse root biomass: log-transformed linear regression or nonlinear regression?
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Jiangshan Lai
Full Text Available Precise estimation of root biomass is important for understanding carbon stocks and dynamics in forests. Traditionally, biomass estimates are based on allometric scaling relationships between stem diameter and coarse root biomass calculated using linear regression (LR on log-transformed data. Recently, it has been suggested that nonlinear regression (NLR is a preferable fitting method for scaling relationships. But while this claim has been contested on both theoretical and empirical grounds, and statistical methods have been developed to aid in choosing between the two methods in particular cases, few studies have examined the ramifications of erroneously applying NLR. Here, we use direct measurements of 159 trees belonging to three locally dominant species in east China to compare the LR and NLR models of diameter-root biomass allometry. We then contrast model predictions by estimating stand coarse root biomass based on census data from the nearby 24-ha Gutianshan forest plot and by testing the ability of the models to predict known root biomass values measured on multiple tropical species at the Pasoh Forest Reserve in Malaysia. Based on likelihood estimates for model error distributions, as well as the accuracy of extrapolative predictions, we find that LR on log-transformed data is superior to NLR for fitting diameter-root biomass scaling models. More importantly, inappropriately using NLR leads to grossly inaccurate stand biomass estimates, especially for stands dominated by smaller trees.
Deep Wavelet Scattering for Quantum Energy Regression
Hirn, Matthew
Physical functionals are usually computed as solutions of variational problems or from solutions of partial differential equations, which may require huge computations for complex systems. Quantum chemistry calculations of ground state molecular energies is such an example. Indeed, if x is a quantum molecular state, then the ground state energy E0 (x) is the minimum eigenvalue solution of the time independent Schrödinger Equation, which is computationally intensive for large systems. Machine learning algorithms do not simulate the physical system but estimate solutions by interpolating values provided by a training set of known examples {(xi ,E0 (xi) } i physical invariants. Linear regressions of E0 over a dictionary Φ ={ϕk } k compute an approximation E 0 as: E 0 (x) =∑kwkϕk (x) , where the weights {wk } k are selected to minimize the error between E0 and E 0 on the training set. The key to such a regression approach then lies in the design of the dictionary Φ. It must be intricate enough to capture the essential variability of E0 (x) over the molecular states x of interest, while simple enough so that evaluation of Φ (x) is significantly less intensive than a direct quantum mechanical computation (or approximation) of E0 (x) . In this talk we present a novel dictionary Φ for the regression of quantum mechanical energies based on the scattering transform of an intermediate, approximate electron density representation ρx of the state x. The scattering transform has the architecture of a deep convolutional network, composed of an alternating sequence of linear filters and nonlinear maps. Whereas in many deep learning tasks the linear filters are learned from the training data, here the physical properties of E0 (invariance to isometric transformations of the state x, stable to deformations of x) are leveraged to design a collection of linear filters ρx *ψλ for an appropriate wavelet ψ. These linear filters are composed with the nonlinear modulus
Image and video restorations via nonlocal kernel regression.
Zhang, Haichao; Yang, Jianchao; Zhang, Yanning; Huang, Thomas S
2013-06-01
A nonlocal kernel regression (NL-KR) model is presented in this paper for various image and video restoration tasks. The proposed method exploits both the nonlocal self-similarity and local structural regularity properties in natural images. The nonlocal self-similarity is based on the observation that image patches tend to repeat themselves in natural images and videos, and the local structural regularity observes that image patches have regular structures where accurate estimation of pixel values via regression is possible. By unifying both properties explicitly, the proposed NL-KR framework is more robust in image estimation, and the algorithm is applicable to various image and video restoration tasks. In this paper, we apply the proposed model to image and video denoising, deblurring, and superresolution reconstruction. Extensive experimental results on both single images and realistic video sequences demonstrate that the proposed framework performs favorably with previous works both qualitatively and quantitatively.
Using Regression Mixture Analysis in Educational Research
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Cody S. Ding
2006-11-01
Full Text Available Conventional regression analysis is typically used in educational research. Usually such an analysis implicitly assumes that a common set of regression parameter estimates captures the population characteristics represented in the sample. In some situations, however, this implicit assumption may not be realistic, and the sample may contain several subpopulations such as high math achievers and low math achievers. In these cases, conventional regression models may provide biased estimates since the parameter estimates are constrained to be the same across subpopulations. This paper advocates the applications of regression mixture models, also known as latent class regression analysis, in educational research. Regression mixture analysis is more flexible than conventional regression analysis in that latent classes in the data can be identified and regression parameter estimates can vary within each latent class. An illustration of regression mixture analysis is provided based on a dataset of authentic data. The strengths and limitations of the regression mixture models are discussed in the context of educational research.
Regression modeling methods, theory, and computation with SAS
Panik, Michael
2009-01-01
Regression Modeling: Methods, Theory, and Computation with SAS provides an introduction to a diverse assortment of regression techniques using SAS to solve a wide variety of regression problems. The author fully documents the SAS programs and thoroughly explains the output produced by the programs.The text presents the popular ordinary least squares (OLS) approach before introducing many alternative regression methods. It covers nonparametric regression, logistic regression (including Poisson regression), Bayesian regression, robust regression, fuzzy regression, random coefficients regression,
A Maximum Likelihood Approach to Least Absolute Deviation Regression
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Yinbo Li
2004-09-01
Full Text Available Least absolute deviation (LAD regression is an important tool used in numerous applications throughout science and engineering, mainly due to the intrinsic robust characteristics of LAD. In this paper, we show that the optimization needed to solve the LAD regression problem can be viewed as a sequence of maximum likelihood estimates (MLE of location. The derived algorithm reduces to an iterative procedure where a simple coordinate transformation is applied during each iteration to direct the optimization procedure along edge lines of the cost surface, followed by an MLE of location which is executed by a weighted median operation. Requiring weighted medians only, the new algorithm can be easily modularized for hardware implementation, as opposed to most of the other existing LAD methods which require complicated operations such as matrix entry manipulations. One exception is Wesolowsky's direct descent algorithm, which among the top algorithms is also based on weighted median operations. Simulation shows that the new algorithm is superior in speed to Wesolowsky's algorithm, which is simple in structure as well. The new algorithm provides a better tradeoff solution between convergence speed and implementation complexity.
Genetic evaluation of European quails by random regression models
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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.
Personality disorders and body weight.
Maclean, Johanna Catherine; Xu, Haiyong; French, Michael T; Ettner, Susan L
2014-01-01
We examine the impact of Axis II personality disorders (PDs) on body weight. PDs are psychiatric conditions that develop early in life from a mixture of genetics and environment, are persistent, and lead to substantial dysfunction for the affected individual. The defining characteristics of PDs conceptually link them with body weight, but the direction of the relationship likely varies across PD type. To investigate these links, we analyze data from Wave II of the National Epidemiological Survey of Alcohol and Related Conditions. We measure body weight with the body mass index (BMI) and a dichotomous indicator for obesity (BMI≥30). We find that women with PDs have significantly higher BMI and are more likely to be obese than otherwise similar women. We find few statistically significant or economically meaningful effects for men. Paranoid, schizotypal, and avoidant PDs demonstrate the strongest adverse impacts on women's body weight while dependent PD may be protective against elevated body weight among men. Findings from unconditional quantile regressions demonstrate a positive gradient between PDs and BMI in that the effects are greater for higher BMI respondents.
Beta blockers & left ventricular hypertrophy regression.
George, Thomas; Ajit, Mullasari S; Abraham, Georgi
2010-01-01
Left ventricular hypertrophy (LVH) particularly in hypertensive patients is a strong predictor of adverse cardiovascular events. Identifying LVH not only helps in the prognostication but also in the choice of therapeutic drugs. The prevalence of LVH is age linked and has a direct correlation to the severity of hypertension. Adequate control of blood pressure, most importantly central aortic pressure and blocking the effects of cardiomyocyte stimulatory growth factors like Angiotensin II helps in regression of LVH. Among the various antihypertensives ACE-inhibitors and angiotensin receptor blockers are more potent than other drugs in regressing LVH. Beta blockers especially the newer cardio selective ones do still have a role in regressing LVH albeit a minor one. A meta-analysis of various studies on LVH regression shows many lacunae. There have been no consistent criteria for defining LVH and documenting LVH regression. This article reviews current evidence on the role of Beta Blockers in LVH regression.
Applied regression analysis a research tool
Pantula, Sastry; Dickey, David
1998-01-01
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understanding of the regression concepts is essential for achieving optimal benefits from a least squares analysis. This book builds on the fundamentals of statistical methods and provides appropriate concepts that will allow a scientist to use least squares as an effective research tool. Applied Regression Analysis is aimed at the scientist who wishes to gain a working knowledge of regression analysis. The basic purpose of this book is to develop an understanding of least squares and related statistical methods without becoming excessively mathematical. It is the outgrowth of more than 30 years of consulting experience with scientists and many years of teaching an applied regression course to graduate students. Applied Regression Analysis serves as an excellent text for a service course on regression for non-statisticians and as a reference for researchers. It also provides a bridge between a two-semester introduction to...
Weighted lasso with data integration.
Bergersen, Linn Cecilie; Glad, Ingrid K; Lyng, Heidi
2011-08-29
The lasso is one of the most commonly used methods for high-dimensional regression, but can be unstable and lacks satisfactory asymptotic properties for variable selection. We propose to use weighted lasso with integrated relevant external information on the covariates to guide the selection towards more stable results. Weighting the penalties with external information gives each regression coefficient a covariate specific amount of penalization and can improve upon standard methods that do not use such information by borrowing knowledge from the external material. The method is applied to two cancer data sets, with gene expressions as covariates. We find interesting gene signatures, which we are able to validate. We discuss various ideas on how the weights should be defined and illustrate how different types of investigations can utilize our method exploiting different sources of external data. Through simulations, we show that our method outperforms the lasso and the adaptive lasso when the external information is from relevant to partly relevant, in terms of both variable selection and prediction.
Takagi, Daisuke; Ikeda, Ken'ichi; Kawachi, Ichiro
2012-11-01
Crime is an important determinant of public health outcomes, including quality of life, mental well-being, and health behavior. A body of research has documented the association between community social capital and crime victimization. The association between social capital and crime victimization has been examined at multiple levels of spatial aggregation, ranging from entire countries, to states, metropolitan areas, counties, and neighborhoods. In multilevel analysis, the spatial boundaries at level 2 are most often drawn from administrative boundaries (e.g., Census tracts in the U.S.). One problem with adopting administrative definitions of neighborhoods is that it ignores spatial spillover. We conducted a study of social capital and crime victimization in one ward of Tokyo city, using a spatial Durbin model with an inverse-distance weighting matrix that assigned each respondent a unique level of "exposure" to social capital based on all other residents' perceptions. The study is based on a postal questionnaire sent to 20-69 years old residents of Arakawa Ward, Tokyo. The response rate was 43.7%. We examined the contextual influence of generalized trust, perceptions of reciprocity, two types of social network variables, as well as two principal components of social capital (constructed from the above four variables). Our outcome measure was self-reported crime victimization in the last five years. In the spatial Durbin model, we found that neighborhood generalized trust, reciprocity, supportive networks and two principal components of social capital were each inversely associated with crime victimization. By contrast, a multilevel regression performed with the same data (using administrative neighborhood boundaries) found generally null associations between neighborhood social capital and crime. Spatial regression methods may be more appropriate for investigating the contextual influence of social capital in homogeneous cultural settings such as Japan.