A generalization of voxel-wise procedures for highdimensional statistical inference using ridge regression

DEFF Research Database (Denmark)

Sjöstrand, Karl; Cardenas, Valerie A.; Larsen, Rasmus

2008-01-01

regression to address this issue, allowing for a gradual introduction of correlation information into the model. We make the connections between ridge regression and voxel-wise procedures explicit and discuss relations to other statistical methods. Results are given on an in-vivo data set of deformation......Whole-brain morphometry denotes a group of methods with the aim of relating clinical and cognitive measurements to regions of the brain. Typically, such methods require the statistical analysis of a data set with many variables (voxels and exogenous variables) paired with few observations (subjects...

IMPROVING CORRELATION FUNCTION FITTING WITH RIDGE REGRESSION: APPLICATION TO CROSS-CORRELATION RECONSTRUCTION

International Nuclear Information System (INIS)

Matthews, Daniel J.; Newman, Jeffrey A.

2012-01-01

Cross-correlation techniques provide a promising avenue for calibrating photometric redshifts and determining redshift distributions using spectroscopy which is systematically incomplete (e.g., current deep spectroscopic surveys fail to obtain secure redshifts for 30%-50% or more of the galaxies targeted). In this paper, we improve on the redshift distribution reconstruction methods from our previous work by incorporating full covariance information into our correlation function fits. Correlation function measurements are strongly covariant between angular or spatial bins, and accounting for this in fitting can yield substantial reduction in errors. However, frequently the covariance matrices used in these calculations are determined from a relatively small set (dozens rather than hundreds) of subsamples or mock catalogs, resulting in noisy covariance matrices whose inversion is ill-conditioned and numerically unstable. We present here a method of conditioning the covariance matrix known as ridge regression which results in a more well behaved inversion than other techniques common in large-scale structure studies. We demonstrate that ridge regression significantly improves the determination of correlation function parameters. We then apply these improved techniques to the problem of reconstructing redshift distributions. By incorporating full covariance information, applying ridge regression, and changing the weighting of fields in obtaining average correlation functions, we obtain reductions in the mean redshift distribution reconstruction error of as much as ∼40% compared to previous methods. We provide a description of POWERFIT, an IDL code for performing power-law fits to correlation functions with ridge regression conditioning that we are making publicly available.

Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models

Energy Technology Data Exchange (ETDEWEB)

Liu, Shujie; Kawamoto, Taisuke; Morita, Osamu [R& D, Safety Science Research, Kao Corporation, Tochigi (Japan); Yoshinari, Kouichi [Department of Molecular Toxicology, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka (Japan); Honda, Hiroshi, E-mail: honda.hiroshi@kao.co.jp [R& D, Safety Science Research, Kao Corporation, Tochigi (Japan)

2017-03-01

Chemical exposure often results in liver hypertrophy in animal tests, characterized by increased liver weight, hepatocellular hypertrophy, and/or cell proliferation. While most of these changes are considered adaptive responses, there is concern that they may be associated with carcinogenesis. In this study, we have employed a toxicogenomic approach using a logistic ridge regression model to identify genes responsible for liver hypertrophy and hypertrophic hepatocarcinogenesis and to develop a predictive model for assessing hypertrophy-inducing compounds. Logistic regression models have previously been used in the quantification of epidemiological risk factors. DNA microarray data from the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System were used to identify hypertrophy-related genes that are expressed differently in hypertrophy induced by carcinogens and non-carcinogens. Data were collected for 134 chemicals (72 non-hypertrophy-inducing chemicals, 27 hypertrophy-inducing non-carcinogenic chemicals, and 15 hypertrophy-inducing carcinogenic compounds). After applying logistic ridge regression analysis, 35 genes for liver hypertrophy (e.g., Acot1 and Abcc3) and 13 genes for hypertrophic hepatocarcinogenesis (e.g., Asns and Gpx2) were selected. The predictive models built using these genes were 94.8% and 82.7% accurate, respectively. Pathway analysis of the genes indicates that, aside from a xenobiotic metabolism-related pathway as an adaptive response for liver hypertrophy, amino acid biosynthesis and oxidative responses appear to be involved in hypertrophic hepatocarcinogenesis. Early detection and toxicogenomic characterization of liver hypertrophy using our models may be useful for predicting carcinogenesis. In addition, the identified genes provide novel insight into discrimination between adverse hypertrophy associated with carcinogenesis and adaptive hypertrophy in risk assessment. - Highlights: • Hypertrophy (H) and hypertrophic

Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models

International Nuclear Information System (INIS)

Liu, Shujie; Kawamoto, Taisuke; Morita, Osamu; Yoshinari, Kouichi; Honda, Hiroshi

2017-01-01

Chemical exposure often results in liver hypertrophy in animal tests, characterized by increased liver weight, hepatocellular hypertrophy, and/or cell proliferation. While most of these changes are considered adaptive responses, there is concern that they may be associated with carcinogenesis. In this study, we have employed a toxicogenomic approach using a logistic ridge regression model to identify genes responsible for liver hypertrophy and hypertrophic hepatocarcinogenesis and to develop a predictive model for assessing hypertrophy-inducing compounds. Logistic regression models have previously been used in the quantification of epidemiological risk factors. DNA microarray data from the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System were used to identify hypertrophy-related genes that are expressed differently in hypertrophy induced by carcinogens and non-carcinogens. Data were collected for 134 chemicals (72 non-hypertrophy-inducing chemicals, 27 hypertrophy-inducing non-carcinogenic chemicals, and 15 hypertrophy-inducing carcinogenic compounds). After applying logistic ridge regression analysis, 35 genes for liver hypertrophy (e.g., Acot1 and Abcc3) and 13 genes for hypertrophic hepatocarcinogenesis (e.g., Asns and Gpx2) were selected. The predictive models built using these genes were 94.8% and 82.7% accurate, respectively. Pathway analysis of the genes indicates that, aside from a xenobiotic metabolism-related pathway as an adaptive response for liver hypertrophy, amino acid biosynthesis and oxidative responses appear to be involved in hypertrophic hepatocarcinogenesis. Early detection and toxicogenomic characterization of liver hypertrophy using our models may be useful for predicting carcinogenesis. In addition, the identified genes provide novel insight into discrimination between adverse hypertrophy associated with carcinogenesis and adaptive hypertrophy in risk assessment. - Highlights: • Hypertrophy (H) and hypertrophic

Interval ridge regression (iRR) as a fast and robust method for quantitative prediction and variable selection applied to edible oil adulteration.

Science.gov (United States)

Jović, Ozren; Smrečki, Neven; Popović, Zora

2016-04-01

A novel quantitative prediction and variable selection method called interval ridge regression (iRR) is studied in this work. The method is performed on six data sets of FTIR, two data sets of UV-vis and one data set of DSC. The obtained results show that models built with ridge regression on optimal variables selected with iRR significantly outperfom models built with ridge regression on all variables in both calibration (6 out of 9 cases) and validation (2 out of 9 cases). In this study, iRR is also compared with interval partial least squares regression (iPLS). iRR outperfomed iPLS in validation (insignificantly in 6 out of 9 cases and significantly in one out of 9 cases for poil, a well known health beneficial nutrient, is studied in this work by mixing it with cheap and widely used oils such as soybean (So) oil, rapeseed (R) oil and sunflower (Su) oil. Binary mixture sets of hempseed oil with these three oils (HSo, HR and HSu) and a ternary mixture set of H oil, R oil and Su oil (HRSu) were considered. The obtained accuracy indicates that using iRR on FTIR and UV-vis data, each particular oil can be very successfully quantified (in all 8 cases RMSEPoil (R(2)>0.99). Copyright © 2015 Elsevier B.V. All rights reserved.

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L. Grown under Three Water Regimes

Directory of Open Access Journals (Sweden)

Javier Hernandez

2015-02-01

Full Text Available Plant breeding based on grain yield (GY is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes of wheat (Triticum aestivum L. through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed, mild water stress (MWS; one irrigation event around booting and full irrigation (FI with mean GYs of 1655, 4739, and 7967 kg∙ha−1, respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R2 increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes.

Real estate value prediction using multivariate regression models

Science.gov (United States)

Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

2017-11-01

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat.

Science.gov (United States)

Pérez-Rodríguez, Paulino; Gianola, Daniel; González-Camacho, Juan Manuel; Crossa, José; Manès, Yann; Dreisigacker, Susanne

2012-12-01

In genome-enabled prediction, parametric, semi-parametric, and non-parametric regression models have been used. This study assessed the predictive ability of linear and non-linear models using dense molecular markers. The linear models were linear on marker effects and included the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B. The non-linear models (this refers to non-linearity on markers) were reproducing kernel Hilbert space (RKHS) regression, Bayesian regularized neural networks (BRNN), and radial basis function neural networks (RBFNN). These statistical models were compared using 306 elite wheat lines from CIMMYT genotyped with 1717 diversity array technology (DArT) markers and two traits, days to heading (DTH) and grain yield (GY), measured in each of 12 environments. It was found that the three non-linear models had better overall prediction accuracy than the linear regression specification. Results showed a consistent superiority of RKHS and RBFNN over the Bayesian LASSO, Bayesian ridge regression, Bayes A, and Bayes B models.

Discriminating between adaptive and carcinogenic liver hypertrophy in rat studies using logistic ridge regression analysis of toxicogenomic data: The mode of action and predictive models.

Science.gov (United States)

Liu, Shujie; Kawamoto, Taisuke; Morita, Osamu; Yoshinari, Kouichi; Honda, Hiroshi

2017-03-01

Chemical exposure often results in liver hypertrophy in animal tests, characterized by increased liver weight, hepatocellular hypertrophy, and/or cell proliferation. While most of these changes are considered adaptive responses, there is concern that they may be associated with carcinogenesis. In this study, we have employed a toxicogenomic approach using a logistic ridge regression model to identify genes responsible for liver hypertrophy and hypertrophic hepatocarcinogenesis and to develop a predictive model for assessing hypertrophy-inducing compounds. Logistic regression models have previously been used in the quantification of epidemiological risk factors. DNA microarray data from the Toxicogenomics Project-Genomics Assisted Toxicity Evaluation System were used to identify hypertrophy-related genes that are expressed differently in hypertrophy induced by carcinogens and non-carcinogens. Data were collected for 134 chemicals (72 non-hypertrophy-inducing chemicals, 27 hypertrophy-inducing non-carcinogenic chemicals, and 15 hypertrophy-inducing carcinogenic compounds). After applying logistic ridge regression analysis, 35 genes for liver hypertrophy (e.g., Acot1 and Abcc3) and 13 genes for hypertrophic hepatocarcinogenesis (e.g., Asns and Gpx2) were selected. The predictive models built using these genes were 94.8% and 82.7% accurate, respectively. Pathway analysis of the genes indicates that, aside from a xenobiotic metabolism-related pathway as an adaptive response for liver hypertrophy, amino acid biosynthesis and oxidative responses appear to be involved in hypertrophic hepatocarcinogenesis. Early detection and toxicogenomic characterization of liver hypertrophy using our models may be useful for predicting carcinogenesis. In addition, the identified genes provide novel insight into discrimination between adverse hypertrophy associated with carcinogenesis and adaptive hypertrophy in risk assessment. Copyright © 2017 Elsevier Inc. All rights reserved.

Variational Ridging in Sea Ice Models

Science.gov (United States)

Roberts, A.; Hunke, E. C.; Lipscomb, W. H.; Maslowski, W.; Kamal, S.

2017-12-01

This work presents the results of a new development to make basin-scale sea ice models aware of the shape, porosity and extent of individual ridges within the pack. We have derived an analytic solution for the Euler-Lagrange equation of individual ridges that accounts for non-conservative forces, and therefore the compressive strength of individual ridges. Because a region of the pack is simply a collection of paths of individual ridges, we are able to solve the Euler-Lagrange equation for a large-scale sea ice field also, and therefore the compressive strength of a region of the pack that explicitly accounts for the macro-porosity of ridged debris. We make a number of assumptions that have simplified the problem, such as treating sea ice as a granular material in ridges, and assuming that bending moments associated with ridging are perturbations around an isostatic state. Regardless of these simplifications, the ridge model is remarkably predictive of macro-porosity and ridge shape, and, because our equations are analytic, they do not require costly computations to solve the Euler-Lagrange equation of ridges on the large scale. The new ridge model is therefore applicable to large-scale sea ice models. We present results from this theoretical development, as well as plans to apply it to the Regional Arctic System Model and a community sea ice code. Most importantly, the new ridging model is particularly useful for pinpointing gaps in our observational record of sea ice ridges, and points to the need for improved measurements of the evolution of porosity of deformed ice in the Arctic and Antarctic. Such knowledge is not only useful for improving models, but also for improving estimates of sea ice volume derived from altimetric measurements of sea ice freeboard.

DRREP: deep ridge regressed epitope predictor.

Science.gov (United States)

Sher, Gene; Zhi, Degui; Zhang, Shaojie

2017-10-03

The ability to predict epitopes plays an enormous role in vaccine development in terms of our ability to zero in on where to do a more thorough in-vivo analysis of the protein in question. Though for the past decade there have been numerous advancements and improvements in epitope prediction, on average the best benchmark prediction accuracies are still only around 60%. New machine learning algorithms have arisen within the domain of deep learning, text mining, and convolutional networks. This paper presents a novel analytically trained and string kernel using deep neural network, which is tailored for continuous epitope prediction, called: Deep Ridge Regressed Epitope Predictor (DRREP). DRREP was tested on long protein sequences from the following datasets: SARS, Pellequer, HIV, AntiJen, and SEQ194. DRREP was compared to numerous state of the art epitope predictors, including the most recently published predictors called LBtope and DMNLBE. Using area under ROC curve (AUC), DRREP achieved a performance improvement over the best performing predictors on SARS (13.7%), HIV (8.9%), Pellequer (1.5%), and SEQ194 (3.1%), with its performance being matched only on the AntiJen dataset, by the LBtope predictor, where both DRREP and LBtope achieved an AUC of 0.702. DRREP is an analytically trained deep neural network, thus capable of learning in a single step through regression. By combining the features of deep learning, string kernels, and convolutional networks, the system is able to perform residue-by-residue prediction of continues epitopes with higher accuracy than the current state of the art predictors.

Graphical evaluation of the ridge-type robust regression estimators in mixture experiments.

Science.gov (United States)

Erkoc, Ali; Emiroglu, Esra; Akay, Kadri Ulas

2014-01-01

In mixture experiments, estimation of the parameters is generally based on ordinary least squares (OLS). However, in the presence of multicollinearity and outliers, OLS can result in very poor estimates. In this case, effects due to the combined outlier-multicollinearity problem can be reduced to certain extent by using alternative approaches. One of these approaches is to use biased-robust regression techniques for the estimation of parameters. In this paper, we evaluate various ridge-type robust estimators in the cases where there are multicollinearity and outliers during the analysis of mixture experiments. Also, for selection of biasing parameter, we use fraction of design space plots for evaluating the effect of the ridge-type robust estimators with respect to the scaled mean squared error of prediction. The suggested graphical approach is illustrated on Hald cement data set.

Prediction of CO2 Emission in China’s Power Generation Industry with Gauss Optimized Cuckoo Search Algorithm and Wavelet Neural Network Based on STIRPAT model with Ridge Regression

Directory of Open Access Journals (Sweden)

Weibo Zhao

2017-12-01

Full Text Available Power generation industry is the key industry of carbon dioxide (CO2 emission in China. Assessing its future CO2 emissions is of great significance to the formulation and implementation of energy saving and emission reduction policies. Based on the Stochastic Impacts by Regression on Population, Affluence and Technology model (STIRPAT, the influencing factors analysis model of CO2 emission of power generation industry is established. The ridge regression (RR method is used to estimate the historical data. In addition, a wavelet neural network (WNN prediction model based on Cuckoo Search algorithm optimized by Gauss (GCS is put forward to predict the factors in the STIRPAT model. Then, the predicted values are substituted into the regression model, and the CO2 emission estimation values of the power generation industry in China are obtained. It’s concluded that population, per capita Gross Domestic Product (GDP, standard coal consumption and thermal power specific gravity are the key factors affecting the CO2 emission from the power generation industry. Besides, the GCS-WNN prediction model has higher prediction accuracy, comparing with other models. Moreover, with the development of science and technology in the future, the CO2 emission growth in the power generation industry will gradually slow down according to the prediction results.

Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP

Directory of Open Access Journals (Sweden)

Jeffrey B. Endelman

2011-11-01

Full Text Available Many important traits in plant breeding are polygenic and therefore recalcitrant to traditional marker-assisted selection. Genomic selection addresses this complexity by including all markers in the prediction model. A key method for the genomic prediction of breeding values is ridge regression (RR, which is equivalent to best linear unbiased prediction (BLUP when the genetic covariance between lines is proportional to their similarity in genotype space. This additive model can be broadened to include epistatic effects by using other kernels, such as the Gaussian, which represent inner products in a complex feature space. To facilitate the use of RR and nonadditive kernels in plant breeding, a new software package for R called rrBLUP has been developed. At its core is a fast maximum-likelihood algorithm for mixed models with a single variance component besides the residual error, which allows for efficient prediction with unreplicated training data. Use of the rrBLUP software is demonstrated through several examples, including the identification of optimal crosses based on superior progeny value. In cross-validation tests, the prediction accuracy with nonadditive kernels was significantly higher than RR for wheat ( L. grain yield but equivalent for several maize ( L. traits.

Intelligent optimization models based on hard-ridge penalty and RBF for forecasting global solar radiation

International Nuclear Information System (INIS)

Jiang, He; Dong, Yao; Wang, Jianzhou; Li, Yuqin

2015-01-01

Highlights: • CS-hard-ridge-RBF and DE-hard-ridge-RBF are proposed to forecast solar radiation. • Pearson and Apriori algorithm are used to analyze correlations between the data. • Hard-ridge penalty is added to reduce the number of nodes in the hidden layer. • CS algorithm and DE algorithm are used to determine the optimal parameters. • Proposed two models have higher forecasting accuracy than RBF and hard-ridge-RBF. - Abstract: Due to the scarcity of equipment and the high costs of maintenance, far fewer observations of solar radiation are made than observations of temperature, precipitation and other weather factors. Therefore, it is increasingly important to study several relevant meteorological factors to accurately forecast solar radiation. For this research, monthly average global solar radiation and 12 meteorological parameters from 1998 to 2010 at four sites in the United States were collected. Pearson correlation coefficients and Apriori association rules were successfully used to analyze correlations between the data, which provided a basis for these relative parameters as input variables. Two effective and innovative methods were developed to forecast monthly average global solar radiation by converting a RBF neural network into a multiple linear regression problem, adding a hard-ridge penalty to reduce the number of nodes in the hidden layer, and applying intelligent optimization algorithms, such as the cuckoo search algorithm (CS) and differential evolution (DE), to determine the optimal center and scale parameters. The experimental results show that the proposed models produce much more accurate forecasts than other models

Modified Regression Correlation Coefficient for Poisson Regression Model

Science.gov (United States)

Kaengthong, Nattacha; Domthong, Uthumporn

2017-09-01

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

A Solution to Separation and Multicollinearity in Multiple Logistic Regression.

Science.gov (United States)

Shen, Jianzhao; Gao, Sujuan

2008-10-01

In dementia screening tests, item selection for shortening an existing screening test can be achieved using multiple logistic regression. However, maximum likelihood estimates for such logistic regression models often experience serious bias or even non-existence because of separation and multicollinearity problems resulting from a large number of highly correlated items. Firth (1993, Biometrika, 80(1), 27-38) proposed a penalized likelihood estimator for generalized linear models and it was shown to reduce bias and the non-existence problems. The ridge regression has been used in logistic regression to stabilize the estimates in cases of multicollinearity. However, neither solves the problems for each other. In this paper, we propose a double penalized maximum likelihood estimator combining Firth's penalized likelihood equation with a ridge parameter. We present a simulation study evaluating the empirical performance of the double penalized likelihood estimator in small to moderate sample sizes. We demonstrate the proposed approach using a current screening data from a community-based dementia study.

Investigating the effects of climate variations on bacillary dysentery incidence in northeast China using ridge regression and hierarchical cluster analysis

Directory of Open Access Journals (Sweden)

Guo Junqiao

2008-09-01

Full Text Available Abstract Background The effects of climate variations on bacillary dysentery incidence have gained more recent concern. However, the multi-collinearity among meteorological factors affects the accuracy of correlation with bacillary dysentery incidence. Methods As a remedy, a modified method to combine ridge regression and hierarchical cluster analysis was proposed for investigating the effects of climate variations on bacillary dysentery incidence in northeast China. Results All weather indicators, temperatures, precipitation, evaporation and relative humidity have shown positive correlation with the monthly incidence of bacillary dysentery, while air pressure had a negative correlation with the incidence. Ridge regression and hierarchical cluster analysis showed that during 1987–1996, relative humidity, temperatures and air pressure affected the transmission of the bacillary dysentery. During this period, all meteorological factors were divided into three categories. Relative humidity and precipitation belonged to one class, temperature indexes and evaporation belonged to another class, and air pressure was the third class. Conclusion Meteorological factors have affected the transmission of bacillary dysentery in northeast China. Bacillary dysentery prevention and control would benefit from by giving more consideration to local climate variations.

Multiattribute shopping models and ridge regression analysis

NARCIS (Netherlands)

Timmermans, H.J.P.

1981-01-01

Policy decisions regarding retailing facilities essentially involve multiple attributes of shopping centres. If mathematical shopping models are to contribute to these decision processes, their structure should reflect the multiattribute character of retailing planning. Examination of existing

Some improved classification-based ridge parameter of Hoerl and ...

African Journals Online (AJOL)

Some improved classification-based ridge parameter of Hoerl and Kennard estimation techniques. ... This assumption is often violated and Ridge Regression estimator introduced by [2]has been identified to be more efficient than ordinary least square (OLS) in handling it. However, it requires a ridge parameter, K, of which ...

On the mean squared error of the ridge estimator of the covariance and precision matrix

NARCIS (Netherlands)

van Wieringen, Wessel N.

2017-01-01

For a suitably chosen ridge penalty parameter, the ridge regression estimator uniformly dominates the maximum likelihood regression estimator in terms of the mean squared error. Analogous results for the ridge maximum likelihood estimators of covariance and precision matrix are presented.

Modeling of flow through fractured tuff at Fran Ridge

International Nuclear Information System (INIS)

Eaton, R.R.; Ho, C.K.; Glass, R.J.; Nicholl, M.J.; Arnold, B.W.

1996-01-01

Numerical studies have modeled an infiltration experiment at Fran Ridge, using the TOUGH2 code, to aid in the selection of computational models for waste repository performance assessment. This study investigates the capabilities of TOUGH2 to simulate transient flows through highly fractured tuff, and provides a possible means of calibrating hydrologic parameters such as effective fracture aperture and fracture-matrix connectivity. Two distinctly different conceptual models were used in the TOUGH2 code, the dual permeability model and the equivalent continuum model. The field experiments involved the infiltration of dyed ponded water in highly fractured tuff. The infiltration observed in the experiment was subsequently modeled using Fran Ridge fracture frequencies, obtained during post-experiment site excavation. Comparison of the TOUGH2 results obtained using the two conceptual models gives insight into their relative strengths and weaknesses

Testing models for the formation of the equatorial ridge on Iapetus via crater counting

Science.gov (United States)

Damptz, Amanda L.; Dombard, Andrew J.; Kirchoff, Michelle R.

2018-03-01

Iapetus's equatorial ridge, visible in global views of the moon, is unique in the Solar System. The formation of this feature is likely attributed to a key event in the evolution of Iapetus, and various models have been proposed as the source of the ridge. By surveying imagery from the Cassini and Voyager missions, this study aims to compile a database of the impact crater population on and around Iapetus's equatorial ridge, assess the relative age of the ridge from differences in cratering between on ridge and off ridge, and test the various models of ridge formation. This work presents a database that contains 7748 craters ranging from 0.83 km to 591 km in diameter. The database includes the study area in which the crater is located, the latitude and longitude of the crater, the major and minor axis lengths, and the azimuthal angle of orientation of the major axis. Analysis of crater orientation over the entire study area reveals that there is no preference for long-axis orientation, particularly in the area with the highest resolution. Comparison of the crater size-frequency distributions show that the crater distribution on the ridge appears to be depleted in craters larger than 16 km with an abruptly enhanced crater population less than 16 km in diameter up to saturation. One possible interpretation is that the ridge is a relatively younger surface with an enhanced small impactor population. Finally, the compiled results are used to examine each ridge formation hypothesis. Based on these results, a model of ridge formation via a tidally disrupted sub-satellite appears most consistent with our interpretation of a younger ridge with an enhanced small impactor population.

Output-Only Modal Parameter Recursive Estimation of Time-Varying Structures via a Kernel Ridge Regression FS-TARMA Approach

Directory of Open Access Journals (Sweden)

Zhi-Sai Ma

2017-01-01

Full Text Available Modal parameter estimation plays an important role in vibration-based damage detection and is worth more attention and investigation, as changes in modal parameters are usually being used as damage indicators. This paper focuses on the problem of output-only modal parameter recursive estimation of time-varying structures based upon parameterized representations of the time-dependent autoregressive moving average (TARMA. A kernel ridge regression functional series TARMA (FS-TARMA recursive identification scheme is proposed and subsequently employed for the modal parameter estimation of a numerical three-degree-of-freedom time-varying structural system and a laboratory time-varying structure consisting of a simply supported beam and a moving mass sliding on it. The proposed method is comparatively assessed against an existing recursive pseudolinear regression FS-TARMA approach via Monte Carlo experiments and shown to be capable of accurately tracking the time-varying dynamics in a recursive manner.

Extracting valley-ridge lines from point-cloud-based 3D fingerprint models.

Science.gov (United States)

Pang, Xufang; Song, Zhan; Xie, Wuyuan

2013-01-01

3D fingerprinting is an emerging technology with the distinct advantage of touchless operation. More important, 3D fingerprint models contain more biometric information than traditional 2D fingerprint images. However, current approaches to fingerprint feature detection usually must transform the 3D models to a 2D space through unwrapping or other methods, which might introduce distortions. A new approach directly extracts valley-ridge features from point-cloud-based 3D fingerprint models. It first applies the moving least-squares method to fit a local paraboloid surface and represent the local point cloud area. It then computes the local surface's curvatures and curvature tensors to facilitate detection of the potential valley and ridge points. The approach projects those points to the most likely valley-ridge lines, using statistical means such as covariance analysis and cross correlation. To finally extract the valley-ridge lines, it grows the polylines that approximate the projected feature points and removes the perturbations between the sampled points. Experiments with different 3D fingerprint models demonstrate this approach's feasibility and performance.

Exploring tectonomagmatic controls on mid-ocean ridge faulting and morphology with 3-D numerical models

Science.gov (United States)

Howell, S. M.; Ito, G.; Behn, M. D.; Olive, J. A. L.; Kaus, B.; Popov, A.; Mittelstaedt, E. L.; Morrow, T. A.

2016-12-01

Previous two-dimensional (2-D) modeling studies of abyssal-hill scale fault generation and evolution at mid-ocean ridges have predicted that M, the ratio of magmatic to total extension, strongly influences the total slip, spacing, and rotation of large faults, as well as the morphology of the ridge axis. Scaling relations derived from these 2-D models broadly explain the globally observed decrease in abyssal hill spacing with increasing ridge spreading rate, as well as the formation of large-offset faults close to the ends of slow-spreading ridge segments. However, these scaling relations do not explain some higher resolution observations of segment-scale variability in fault spacing along the Chile Ridge and the Mid-Atlantic Ridge, where fault spacing shows no obvious correlation with M. This discrepancy between observations and 2-D model predictions illuminates the need for three-dimensional (3-D) numerical models that incorporate the effects of along-axis variations in lithospheric structure and magmatic accretion. To this end, we use the geodynamic modeling software LaMEM to simulate 3-D tectono-magmatic interactions in a visco-elasto-plastic lithosphere under extension. We model a single ridge segment subjected to an along-axis gradient in the rate of magma injection, which is simulated by imposing a mass source in a plane of model finite volumes beneath the ridge axis. Outputs of interest include characteristic fault offset, spacing, and along-axis gradients in seafloor morphology. We also examine the effects of along-axis variations in lithospheric thickness and off-axis thickening rate. The main objectives of this study are to quantify the relative importance of the amount of magmatic extension and the local lithospheric structure at a given along-axis location, versus the importance of along-axis communication of lithospheric stresses on the 3-D fault evolution and morphology of intermediate-spreading-rate ridges.

Global Models of Ridge-Push Force, Geoid, and Lithospheric Strength of Oceanic plates

Science.gov (United States)

Mahatsente, Rezene

2017-12-01

An understanding of the transmission of ridge-push related stresses in the interior of oceanic plates is important because ridge-push force is one of the principal forces driving plate motion. Here, I assess the transmission of ridge-push related stresses in oceanic plates by comparing the magnitude of the ridge-push force to the integrated strength of oceanic plates. The strength is determined based on plate cooling and rheological models. The strength analysis includes low-temperature plasticity (LTP) in the upper mantle and assumes a range of possible tectonic conditions and rheology in the plates. The ridge-push force has been derived from the thermal state of oceanic lithosphere, seafloor depth and crustal age data. The results of modeling show that the transmission of ridge-push related stresses in oceanic plates mainly depends on rheology and predominant tectonic conditions. If a lithosphere has dry rheology, the estimated strength is higher than the ridge-push force at all ages for compressional tectonics and at old ages (>75 Ma) for extension. Therefore, under such conditions, oceanic plates may not respond to ridge-push force by intraplate deformation. Instead, the plates may transmit the ridge-push related stress in their interior. For a wet rheology, however, the strength of young lithosphere (stress may dissipate in the interior of oceanic plates and diffuses by intraplate deformation. The state of stress within a plate depends on the balance of far-field and intraplate forces.

THE STATISTICAL MODEL OF PRESSURE RIDGE MORPHOMETRY ON THE NORTHEAST SHELF OF SAKHALIN ISLAND

Directory of Open Access Journals (Sweden)

E. U. Mironov

2012-01-01

Full Text Available The work presents characteristics on geometry and inner structure of ice ridges investigated at offshore the northeast coast of SakhalinIsland. A formula was obtained which allows one to calculate the ice ridge keel depth by the height of its sail. Plots of the probability distribution density for ice ridge characteristics are given. A model of morphometry of a mean statistical ice ridge was constructed, and its mass is determined. Factors influencing the hydrostatic ice ridge equilibrium are considered.

Europan double ridge morphometry as a test of formation models

Science.gov (United States)

Dameron, Ashley C.; Burr, Devon M.

2018-05-01

Double ridges on the Jovian satellite Europa consist of two parallel ridges with a central trough. Although these features are nearly ubiquitous on Europa, their formation mechanism(s) is (are) not yet well-understood. Previous hypotheses for their formation can be divided into two groups based on 1) the expected interior slope angles and 2) the magnitude of interior/exterior slope symmetry. The published hypotheses in the first ("fracture") group entail brittle deformation of the crust, either by diapirism, shear heating, or buckling due to compression. Because these mechanisms imply uplift of near-vertical fractures, their predicted interior slopes are steeper than the angle of repose (AOR) with shallower exterior slopes. The second ("flow") group includes cryosedimentary and cryovolcanic processes - explosive or effusive cryovolcanism and tidal squeezing -, which are predicted to form ridge slopes at or below the AOR. Explosive cryovolcanism would form self-symmetric ridges, whereas effusive cryolavas and cryo-sediments deposited during tidal squeezing would likely not exhibit slope symmetry. To distinguish between these two groups of hypothesized formation mechanisms, we derived measurements of interior slope angle and interior/exterior slope symmetry at multiple locations on Europa through analysis of data from the Galileo Solid State Imaging (SSI) camera. Two types of data were used: i) elevation data from five stereo-pair digital elevation models (DEMs) covering four ridges (580 individual measurements), and ii) ridge shadow length measurements taken on individual images over 40 ridges (200 individual measurements). Our results shows that slopes measured on our DEMs, located in the Cilix and Banded Plains regions, typically fall below the AOR, and slope symmetry is dominant. Two different shadow measurement techniques implemented to calculate interior slopes yielded slope angles that also fall below the AOR. The shallow interior slopes derived from both

Learning a peptide-protein binding affinity predictor with kernel ridge regression

Science.gov (United States)

2013-01-01

Background The cellular function of a vast majority of proteins is performed through physical interactions with other biomolecules, which, most of the time, are other proteins. Peptides represent templates of choice for mimicking a secondary structure in order to modulate protein-protein interaction. They are thus an interesting class of therapeutics since they also display strong activity, high selectivity, low toxicity and few drug-drug interactions. Furthermore, predicting peptides that would bind to a specific MHC alleles would be of tremendous benefit to improve vaccine based therapy and possibly generate antibodies with greater affinity. Modern computational methods have the potential to accelerate and lower the cost of drug and vaccine discovery by selecting potential compounds for testing in silico prior to biological validation. Results We propose a specialized string kernel for small bio-molecules, peptides and pseudo-sequences of binding interfaces. The kernel incorporates physico-chemical properties of amino acids and elegantly generalizes eight kernels, comprised of the Oligo, the Weighted Degree, the Blended Spectrum, and the Radial Basis Function. We provide a low complexity dynamic programming algorithm for the exact computation of the kernel and a linear time algorithm for it’s approximation. Combined with kernel ridge regression and SupCK, a novel binding pocket kernel, the proposed kernel yields biologically relevant and good prediction accuracy on the PepX database. For the first time, a machine learning predictor is capable of predicting the binding affinity of any peptide to any protein with reasonable accuracy. The method was also applied to both single-target and pan-specific Major Histocompatibility Complex class II benchmark datasets and three Quantitative Structure Affinity Model benchmark datasets. Conclusion On all benchmarks, our method significantly (p-value ≤ 0.057) outperforms the current state-of-the-art methods at predicting

Patch behaviour and predictability properties of modelled finite-amplitude sand ridges on the inner shelf

Directory of Open Access Journals (Sweden)

N. C. Vis-Star

2008-12-01

Full Text Available The long-term evolution of shoreface-connected sand ridges is investigated with a nonlinear spectral model which governs the dynamics of waves, currents, sediment transport and the bed level on the inner shelf. Wave variables are calculated with a shoaling-refraction model instead of using a parameterisation. The spectral model describes the time evolution of amplitudes of known eigenmodes of the linearised system. Bottom pattern formation occurs if the transverse bottom slope of the inner shelf, β, exceeds a critical value β_c. For fixed model parameters the sensitivity of the properties of modelled sand ridges to changes in the number (N−1 of resolved subharmonics (of the initially fastest growing mode is investigated. For any N the model shows the growth and subsequent saturation of the height of the sand ridges. The saturation time scale is several thousands of years, which suggests that observed sand ridges have not reached their saturated stage yet. The migration speed of the ridges and the average longshore spacing between successive crests in the saturated state differ from those in the initial state. Analysis of the potential energy balance of the ridges reveals that bed slope-induced sediment transport is crucial for the saturation process. In the transient stage the shoreface-connected ridges occur in patches. The overall characteristics of the bedforms (saturation time, final maximum height, average longshore spacing, migration speed hardly vary with N. However, individual time series of modal amplitudes and bottom patterns strongly depend on N, thereby implying that the detailed evolution of sand ridges can only be predicted over a limited time interval. Additional experiments show that the critical bed slope β_c increases with larger offshore angles of wave incidence, larger offshore wave heights and longer wave periods, and that the corresponding maximum height of the ridges

The Collinearity Free and Bias Reduced Regression Estimation Project: The Theory of Normalization Ridge Regression. Report No. 2.

Science.gov (United States)

Bulcock, J. W.; And Others

Multicollinearity refers to the presence of highly intercorrelated independent variables in structural equation models, that is, models estimated by using techniques such as least squares regression and maximum likelihood. There is a problem of multicollinearity in both the natural and social sciences where theory formulation and estimation is in…

Contaminant transport model validation: The Oak Ridge Reservation

International Nuclear Information System (INIS)

Lee, R.R.; Ketelle, R.H.

1988-09-01

In the complex geologic setting on the Oak Ridge Reservation, hydraulic conductivity is anisotropic and flow is strongly influenced by an extensive and largely discontinuous fracture network. Difficulties in describing and modeling the aquifer system prompted a study to obtain aquifer property data to be used in a groundwater flow model validation experiment. Characterization studies included the performance of an extensive suite of aquifer test within a 600-square-meter area to obtain aquifer property values to describe the flow field in detail. Following aquifer test, a groundwater tracer test was performed under ambient conditions to verify the aquifer analysis. Tracer migration data in the near-field were used in model calibration to predict tracer arrival time and concentration in the far-field. Despite the extensive aquifer testing, initial modeling inaccurately predicted tracer migration direction. Initial tracer migration rates were consistent with those predicted by the model; however, changing environmental conditions resulted in an unanticipated decay in tracer movement. Evaluation of the predictive accuracy of groundwater flow and contaminant transport models on the Oak Ridge Reservation depends on defining the resolution required, followed by field testing and model grid definition at compatible scales. The use of tracer tests, both as a characterization method and to verify model results, provides the highest level of resolution of groundwater flow characteristics. 3 refs., 4 figs

Regression modeling of ground-water flow

Science.gov (United States)

Cooley, R.L.; Naff, R.L.

1985-01-01

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

Regression Models for Market-Shares

DEFF Research Database (Denmark)

Birch, Kristina; Olsen, Jørgen Kai; Tjur, Tue

2005-01-01

On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put on the interpretat......On the background of a data set of weekly sales and prices for three brands of coffee, this paper discusses various regression models and their relation to the multiplicative competitive-interaction model (the MCI model, see Cooper 1988, 1993) for market-shares. Emphasis is put...... on the interpretation of the parameters in relation to models for the total sales based on discrete choice models.Key words and phrases. MCI model, discrete choice model, market-shares, price elasitcity, regression model....

Two-dimensional chronostratigraphic modelling of OSL ages from recent beach-ridge deposits, SE Australia

DEFF Research Database (Denmark)

Tamura, Toru; Cunningham, Alastair C.; Oliver, Thomas S.N.

2018-01-01

Optically-stimulated luminesecne (OSL) dating, in concert with two-dimensional ground-penetrating radar (GPR) profiling, has contributed to significant advances in our understanding of beach-ridge systems and other sedimentary landforms in various settings. For recent beach-ridges, the good OSL...... samples may be larger than the difference in sample ages. Age inversions can be avoided, however, if the stratigraphic constraints are included in the age estimation process. Here, we create a custom Bayesian chronological model for a recent (..., for direct comparison with a GPR profile. The model includes a full ‘burial-dose model’ for each sample and a dose rate term with the modelled ages constrained by the vertical and shore-normal sample order. The modelled ages are visualized by plotting isochrones on the beach-ridge cross section...

A Seemingly Unrelated Poisson Regression Model

OpenAIRE

King, Gary

1989-01-01

This article introduces a new estimator for the analysis of two contemporaneously correlated endogenous event count variables. This seemingly unrelated Poisson regression model (SUPREME) estimator combines the efficiencies created by single equation Poisson regression model estimators and insights from "seemingly unrelated" linear regression models.

Panel Smooth Transition Regression Models

DEFF Research Database (Denmark)

González, Andrés; Terasvirta, Timo; Dijk, Dick van

We introduce the panel smooth transition regression model. This new model is intended for characterizing heterogeneous panels, allowing the regression coefficients to vary both across individuals and over time. Specifically, heterogeneity is allowed for by assuming that these coefficients are bou...

Interpretation of commonly used statistical regression models.

Science.gov (United States)

Kasza, Jessica; Wolfe, Rory

2014-01-01

A review of some regression models commonly used in respiratory health applications is provided in this article. Simple linear regression, multiple linear regression, logistic regression and ordinal logistic regression are considered. The focus of this article is on the interpretation of the regression coefficients of each model, which are illustrated through the application of these models to a respiratory health research study. © 2013 The Authors. Respirology © 2013 Asian Pacific Society of Respirology.

Three-dimensional modeling of flow through fractured tuff at Fran Ridge

International Nuclear Information System (INIS)

Eaton, R.R.; Ho, C.K.; Glass, RJ.; Nicholl, M.J.; Arnold, B.W.

1996-09-01

Numerical studies have been made of an infiltration experiment at Fran Ridge using the TOUGH2 code to aid in the selection of computational models for performance assessment. The exercise investigates the capabilities of TOUGH2 to model transient flows through highly fractured tuff and provides a possible means of calibration. Two distinctly different conceptual models were used in the TOUGH2 code, the dual permeability model and the equivalent continuum model. The infiltration test modeled involved the infiltration of dyed ponded water for 36 minutes. The 205 gallon infiltration of water observed in the experiment was subsequently modeled using measured Fran Ridge fracture frequencies, and a specified fracture aperture of 285 microm. The dual permeability formulation predicted considerable infiltration along the fracture network, which was in agreement with the experimental observations. As expected, al fracture penetration of the infiltrating water was calculated using the equivalent continuum model, thus demonstrating that this model is not appropriate for modeling the highly transient experiment. It is therefore recommended that the dual permeability model be given priority when computing high-flux infiltration for use in performance assessment studies

Three-dimensional modeling of flow through fractured tuff at Fran Ridge

International Nuclear Information System (INIS)

Eaton, R.R.; Ho, C.K.; Glass, R.J.; Nicholl, M.J.; Arnold, B.W.

1996-01-01

Numerical studies have been made of an infiltration experiment at Fran Ridge using the TOUGH2 code to aid in the selection of computational models for performance assessment. The exercise investigates the capabilities of TOUGH2 to model transient flows through highly fractured tuff and provides a possible means of calibration. Two distinctly different conceptual models were used in the TOUGH2 code, the dual permeability model and the equivalent continuum model. The infiltration test modeled involved the infiltration of dyed ponded water for 36 minutes. The 205 gallon filtration of water observed in the experiment was subsequently modeled using measured Fran Ridge fracture frequencies, and a specified fracture aperture of 285 μm. The dual permeability formulation predicted considerable infiltration along the fracture network, which was in agreement with the experimental observations. As expected, minimal fracture penetration of the infiltrating water was calculated using the equivalent continuum model, thus demonstrating that this model is not appropriate for modeling the highly transient experiment. It is therefore recommended that the dual permeability model be given priority when computing high-flux infiltration for use in performance assessment studies

Poisson Mixture Regression Models for Heart Disease Prediction.

Science.gov (United States)

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

Science.gov (United States)

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Effects of sea level rise on the formation and drowning of shoreface-connected sand ridges, a model study

Science.gov (United States)

Nnafie, A.; de Swart, H. E.; Calvete, D.; Garnier, R.

2014-06-01

Shoreface-connected sand ridges occur on many storm-dominated inner shelves. These rhythmic features have an along-shelf spacing of 2-10 km, a height of 1-12 m, they evolve on timescales of centuries and they migrate several meters per year. An idealized model is used to study the impact of sea level rise on the characteristics of the sand ridges during their initial and long-term evolution. Different scenarios (rates of sea level rise, geometry of inner shelf) are examined. Results show that with increasing sea level the height of sand ridges increases and their migration decreases until they eventually drown. This latter occurs when the near-bed wave orbital velocity drops below the critical velocity for erosion of sediment. In contrast, in the absence of sea level rise, the model simulates shoreface-connected sand ridges with constant heights and migration rates. Model results furthermore indicate that sand ridges do not form if the rate of sea level rise is too high, or if the initial depth of the inner shelf is too small. A larger transverse bottom slope enhances growth and height of sand ridges and they drown quicker. When shoreface retreat due to sea level rise is considered, new ridges form in the landward part of the inner shelf, while ridges on the antecedent part of the shelf become less active and ultimately drown. Only if sea level rise is accounted for, merging of ridges is reduced such that multiple ridges occur in the end state, thereby yielding a better agreement with observations. The physical mechanisms responsible for these findings are also explained.

Comparison of some biased estimation methods (including ordinary subset regression) in the linear model

Science.gov (United States)

Sidik, S. M.

1975-01-01

Ridge, Marquardt's generalized inverse, shrunken, and principal components estimators are discussed in terms of the objectives of point estimation of parameters, estimation of the predictive regression function, and hypothesis testing. It is found that as the normal equations approach singularity, more consideration must be given to estimable functions of the parameters as opposed to estimation of the full parameter vector; that biased estimators all introduce constraints on the parameter space; that adoption of mean squared error as a criterion of goodness should be independent of the degree of singularity; and that ordinary least-squares subset regression is the best overall method.

Mixture of Regression Models with Single-Index

OpenAIRE

Xiang, Sijia; Yao, Weixin

2016-01-01

In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for...

An Additive-Multiplicative Cox-Aalen Regression Model

DEFF Research Database (Denmark)

Scheike, Thomas H.; Zhang, Mei-Jie

2002-01-01

Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; Cox regression; survival analysis; time-varying effects...

[From clinical judgment to linear regression model.

Science.gov (United States)

Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

Regression models of reactor diagnostic signals

International Nuclear Information System (INIS)

Vavrin, J.

1989-01-01

The application is described of an autoregression model as the simplest regression model of diagnostic signals in experimental analysis of diagnostic systems, in in-service monitoring of normal and anomalous conditions and their diagnostics. The method of diagnostics is described using a regression type diagnostic data base and regression spectral diagnostics. The diagnostics is described of neutron noise signals from anomalous modes in the experimental fuel assembly of a reactor. (author)

Forecasting with Dynamic Regression Models

CERN Document Server

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.

Categorical regression dose-response modeling

Science.gov (United States)

The goal of this training is to provide participants with training on the use of the U.S. EPA’s Categorical Regression soft¬ware (CatReg) and its application to risk assessment. Categorical regression fits mathematical models to toxicity data that have been assigned ord...

Mixed-effects regression models in linguistics

CERN Document Server

Heylen, Kris; Geeraerts, Dirk

2018-01-01

When data consist of grouped observations or clusters, and there is a risk that measurements within the same group are not independent, group-specific random effects can be added to a regression model in order to account for such within-group associations. Regression models that contain such group-specific random effects are called mixed-effects regression models, or simply mixed models. Mixed models are a versatile tool that can handle both balanced and unbalanced datasets and that can also be applied when several layers of grouping are present in the data; these layers can either be nested or crossed.  In linguistics, as in many other fields, the use of mixed models has gained ground rapidly over the last decade. This methodological evolution enables us to build more sophisticated and arguably more realistic models, but, due to its technical complexity, also introduces new challenges. This volume brings together a number of promising new evolutions in the use of mixed models in linguistics, but also addres...

Moderation analysis using a two-level regression model.

Science.gov (United States)

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

FEAST 3.1: finite-element modeling of sheath deformation such as longitudinal ridging and collapse into axial gap

Energy Technology Data Exchange (ETDEWEB)

Wang, X.; Xu, Z.; Kim, Y-S.; Lai, L.; Cheng, G.; Xu, S. [Atomic Energy of Canada Limited, Mississauga, Ontario (Canada)

2010-07-01

During normal operation, the collapsible CANDU® fuel sheath deforms, especially, it may deform into longitudinal ridges or collapse instantaneously into the axial gaps between the end pellet and endcap or between two neighbouring pellets. These phenomena occur under certain conditions, such as the coolant pressure exceeding critical pressures for longitudinal ridging or axial collapse. Both longitudinal ridging and axial collapse phenomena result from plastic instability in the sheath under coolant pressure. Longitudinal ridging features one or multiple lobes or 'ridges' (outward from the sheath surface) formed along the sheath in the longitudinal direction. Axial collapse features a 'valley' around the sheath circumference. Both phenomena can lead to sheath overstrain, which in turn potentially leads to sheath failure. The LONGER code, which contains empirical correlations, has been used to predict the critical pressures for these two sheath deformation phenomena. To study fuel behaviour outside of the application ranges of the LONGER empirical correlations, a mechanistic model is needed. FEAST (Finite Element Analysis for Stresses) is an AECL computer code used to assess the structural integrity of the CANDU fuel element. The FEAST code has recently been developed (to Version 3.1) to model processes occurring during longitudinal ridge formation and instantaneous collapse into the axial gap. The new models include those for geometric non-linearity (large deformation, large material rotation), non-linear stress-strain curve for plastic deformation, Zr-4 sheath creep law, and variable Young’s Modulus etc. This paper describes the mechanistic model (FEAST 3.1) development for analyses of longitudinal ridging and instantaneous collapse into axial gap, and the comparison with the results from empirical correlations in LONGER. (author)

FEAST 3.1: finite-element modeling of sheath deformation such as longitudinal ridging and collapse into axial gap

International Nuclear Information System (INIS)

Wang, X.; Xu, Z.; Kim, Y-S.; Lai, L.; Cheng, G.; Xu, S.

2010-01-01

During normal operation, the collapsible CANDU® fuel sheath deforms, especially, it may deform into longitudinal ridges or collapse instantaneously into the axial gaps between the end pellet and endcap or between two neighbouring pellets. These phenomena occur under certain conditions, such as the coolant pressure exceeding critical pressures for longitudinal ridging or axial collapse. Both longitudinal ridging and axial collapse phenomena result from plastic instability in the sheath under coolant pressure. Longitudinal ridging features one or multiple lobes or 'ridges' (outward from the sheath surface) formed along the sheath in the longitudinal direction. Axial collapse features a 'valley' around the sheath circumference. Both phenomena can lead to sheath overstrain, which in turn potentially leads to sheath failure. The LONGER code, which contains empirical correlations, has been used to predict the critical pressures for these two sheath deformation phenomena. To study fuel behaviour outside of the application ranges of the LONGER empirical correlations, a mechanistic model is needed. FEAST (Finite Element Analysis for Stresses) is an AECL computer code used to assess the structural integrity of the CANDU fuel element. The FEAST code has recently been developed (to Version 3.1) to model processes occurring during longitudinal ridge formation and instantaneous collapse into the axial gap. The new models include those for geometric non-linearity (large deformation, large material rotation), non-linear stress-strain curve for plastic deformation, Zr-4 sheath creep law, and variable Young’s Modulus etc. This paper describes the mechanistic model (FEAST 3.1) development for analyses of longitudinal ridging and instantaneous collapse into axial gap, and the comparison with the results from empirical correlations in LONGER. (author)

A comparison of regression algorithms for wind speed forecasting at Alexander Bay

CSIR Research Space (South Africa)

Botha, Nicolene

2016-12-01

Full Text Available to forecast 1 to 24 hours ahead, in hourly intervals. Predictions are performed on a wind speed time series with three machine learning regression algorithms, namely support vector regression, ordinary least squares and Bayesian ridge regression. The resulting...

Variable selection and model choice in geoadditive regression models.

Science.gov (United States)

Kneib, Thomas; Hothorn, Torsten; Tutz, Gerhard

2009-06-01

Model choice and variable selection are issues of major concern in practical regression analyses, arising in many biometric applications such as habitat suitability analyses, where the aim is to identify the influence of potentially many environmental conditions on certain species. We describe regression models for breeding bird communities that facilitate both model choice and variable selection, by a boosting algorithm that works within a class of geoadditive regression models comprising spatial effects, nonparametric effects of continuous covariates, interaction surfaces, and varying coefficients. The major modeling components are penalized splines and their bivariate tensor product extensions. All smooth model terms are represented as the sum of a parametric component and a smooth component with one degree of freedom to obtain a fair comparison between the model terms. A generic representation of the geoadditive model allows us to devise a general boosting algorithm that automatically performs model choice and variable selection.

Regression tools for CO2 inversions: application of a shrinkage estimator to process attribution

International Nuclear Information System (INIS)

Shaby, Benjamin A.; Field, Christopher B.

2006-01-01

In this study we perform an atmospheric inversion based on a shrinkage estimator. This method is used to estimate surface fluxes of CO 2 , first partitioned according to constituent geographic regions, and then according to constituent processes that are responsible for the total flux. Our approach differs from previous approaches in two important ways. The first is that the technique of linear Bayesian inversion is recast as a regression problem. Seen as such, standard regression tools are employed to analyse and reduce errors in the resultant estimates. A shrinkage estimator, which combines standard ridge regression with the linear 'Bayesian inversion' model, is introduced. This method introduces additional bias into the model with the aim of reducing variance such that errors are decreased overall. Compared with standard linear Bayesian inversion, the ridge technique seems to reduce both flux estimation errors and prediction errors. The second divergence from previous studies is that instead of dividing the world into geographically distinct regions and estimating the CO 2 flux in each region, the flux space is divided conceptually into processes that contribute to the total global flux. Formulating the problem in this manner adds to the interpretability of the resultant estimates and attempts to shed light on the problem of attributing sources and sinks to their underlying mechanisms

The MIDAS Touch: Mixed Data Sampling Regression Models

OpenAIRE

Ghysels, Eric; Santa-Clara, Pedro; Valkanov, Rossen

2004-01-01

We introduce Mixed Data Sampling (henceforth MIDAS) regression models. The regressions involve time series data sampled at different frequencies. Technically speaking MIDAS models specify conditional expectations as a distributed lag of regressors recorded at some higher sampling frequencies. We examine the asymptotic properties of MIDAS regression estimation and compare it with traditional distributed lag models. MIDAS regressions have wide applicability in macroeconomics and �nance.

Waste management systems model for energy systems sites on the Oak Ridge Reservation

International Nuclear Information System (INIS)

Rodgers, B.R.; Nehls, J.W. Jr.; Rivera, A.L.; Pechin, W.H.; Genung, R.K.

1987-01-01

The Oak Ridge Model (ORM) is a DOE/Oak Ridge Operations (DOE/ORO) initiative which involves regulators and the public at the problem definition stage to attempt to reach a consensus on acceptable approaches to the solution of waste management problems. Once the approaches have been defined, the private sector participates in identifying and demonstrating the technologies to be employed. The Waste Management Systems Model (WSM), a major part of the ORM, is discussed in this paper. It can be generically described as employing a number of computer models to: 1. determine waste management requirements (e.g., capabilities and capacities); 2. develop scenarios that respond to changes in requirements and that evaluate alternatives as they become available; 3. compare these scenarios technically, operationally, and financially; and 4. select the most promising technologies for demonstration. 12 figures

A deep structural ridge beneath central India

Science.gov (United States)

Agrawal, P. K.; Thakur, N. K.; Negi, J. G.

A joint-inversion of magnetic satellite (MAGSAT) and free air gravity data has been conducted to quantitatively investigate the cause for Bouguer gravity anomaly over Central Indian plateaus and possible fold consequences beside Himalayan zone in the Indian sub-continent due to collision between Indian and Eurasian plates. The appropriate inversion with 40 km crustal depth model has delineated after discriminating high density and magnetisation models, for the first time, about 1500 km long hidden ridge structure trending NW-SE. The structure is parallel to Himalayan fold axis and the Indian Ocean ridge in the Arabian Sea. A quantitative relief model across a representative anomaly profile confirms the ridge structure with its highest point nearly 6 km higher than the surrounding crustal level in peninsular India. The ridge structure finds visible support from the astro-geoidal contours.

Introduction to the use of regression models in epidemiology.

Science.gov (United States)

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

Development of a hydrological model for simulation of runoff from catchments unbounded by ridge lines

Science.gov (United States)

Vema, Vamsikrishna; Sudheer, K. P.; Chaubey, I.

2017-08-01

Watershed hydrological models are effective tools for simulating the hydrological processes in the watershed. Although there are a plethora of hydrological models, none of them can be directly applied to make water conservation decisions in irregularly bounded areas that do not confirm to topographically defined ridge lines. This study proposes a novel hydrological model that can be directly applied to any catchment, with or without ridge line boundaries. The model is based on the water balance concept, and a linear function concept to approximate the cross-boundary flow from upstream areas to the administrative catchment under consideration. The developed model is tested in 2 watersheds - Riesel Experimental Watershed and a sub-basin of Cedar Creek Watershed in Texas, USA. Hypothetical administrative catchments that did not confirm to the location of ridge lines were considered for verifying the efficacy of the model for hydrologic simulations. The linear function concept used to account the cross boundary flow was based on the hypothesis that the flow coming from outside the boundary to administrative area was proportional to the flow generated in the boundary grid cell. The model performance was satisfactory with an NSE and r2 of ≥0.80 and a PBIAS of administrative catchments of the watersheds were in good agreement with the observed hydrographs, indicating a satisfactory performance of the model in the administratively bounded areas.

Model-based Quantile Regression for Discrete Data

KAUST Repository

Padellini, Tullia

2018-04-10

Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite the fact that this leads to a proper posterior for the regression coefficients, the resulting posterior variance is however affected by an unidentifiable parameter, hence any inferential procedure beside point estimation is unreliable. We propose a model-based approach for quantile regression that considers quantiles of the generating distribution directly, and thus allows for a proper uncertainty quantification. We then create a link between quantile regression and generalised linear models by mapping the quantiles to the parameter of the response variable, and we exploit it to fit the model with R-INLA. We extend it also in the case of discrete responses, where there is no 1-to-1 relationship between quantiles and distribution\\'s parameter, by introducing continuous generalisations of the most common discrete variables (Poisson, Binomial and Negative Binomial) to be exploited in the fitting.

Origin of lavas from the Ninetyeast Ridge, Eastern Indian Ocean: An evaluation of fractional crystallization models

Energy Technology Data Exchange (ETDEWEB)

Ludden, J.N.; Thompson, G.; Bryan, W.B.; Frey, F.A.

1980-08-10

Ferrobasalts from DSDP sites 214 and 216 on the Ninetyeast Ridge are characterized by high absolute iron (FeO>12.9 wt %), FeO/MgO>1.9, and TiO/sub 2/>2.0 wt %. Their trace element abundances indicate a tholeiitic affinity; however, they are distinct from midocean ridge incompatible element-depleted tholeiites owing to higher contents of Ba, Zr, and Sr and flat to slightly light-REE-enriched, chondrite-normalized REE patterns. Calculations using major and trace element abundances and phase compositions are generally consistent with a model relating most major elements and phase compositions in site 214 and 216 ferrobasalts by fractionation of clinopyroxene and plagio-class. However, some incompatible element abundances for site 216 basalts are not consistent with the fractional crystallization models. Baslats from site 214 can be related to andesitic rocks from the same site by fractionating clinopyroxene, plagioclase and titanomagnetite. Site 254 basalts, at the southern end of the Ninetyeast Ridge, and island tholeiites in the southern Indian Ocean (Amsterdam-St. Paul or Kerguelen-Heard volcanic provinces) possibly represent the most recent activity associated with a hot spot forming the Ninetyeast Ridge. These incompatible-element-enriched tholeiites have major element compositions consistent with those expected for a parental liquid for the site 214 and 216 ferrobasalts. However, differences in the trace element contents of the basalts from the Ninetyeast Ridge sites are not consistent with simple fractional crystallization derivation but require either a complex melting model or a heterogeneous mantle source.

Variable importance in latent variable regression models

NARCIS (Netherlands)

Kvalheim, O.M.; Arneberg, R.; Bleie, O.; Rajalahti, T.; Smilde, A.K.; Westerhuis, J.A.

2014-01-01

The quality and practical usefulness of a regression model are a function of both interpretability and prediction performance. This work presents some new graphical tools for improved interpretation of latent variable regression models that can also assist in improved algorithms for variable

A simple approach to power and sample size calculations in logistic regression and Cox regression models.

Science.gov (United States)

Vaeth, Michael; Skovlund, Eva

2004-06-15

For a given regression problem it is possible to identify a suitably defined equivalent two-sample problem such that the power or sample size obtained for the two-sample problem also applies to the regression problem. For a standard linear regression model the equivalent two-sample problem is easily identified, but for generalized linear models and for Cox regression models the situation is more complicated. An approximately equivalent two-sample problem may, however, also be identified here. In particular, we show that for logistic regression and Cox regression models the equivalent two-sample problem is obtained by selecting two equally sized samples for which the parameters differ by a value equal to the slope times twice the standard deviation of the independent variable and further requiring that the overall expected number of events is unchanged. In a simulation study we examine the validity of this approach to power calculations in logistic regression and Cox regression models. Several different covariate distributions are considered for selected values of the overall response probability and a range of alternatives. For the Cox regression model we consider both constant and non-constant hazard rates. The results show that in general the approach is remarkably accurate even in relatively small samples. Some discrepancies are, however, found in small samples with few events and a highly skewed covariate distribution. Comparison with results based on alternative methods for logistic regression models with a single continuous covariate indicates that the proposed method is at least as good as its competitors. The method is easy to implement and therefore provides a simple way to extend the range of problems that can be covered by the usual formulas for power and sample size determination. Copyright 2004 John Wiley & Sons, Ltd.

Regression modeling methods, theory, and computation with SAS

CERN Document Server

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 regression approach for Zircaloy-2 in-reactor creep constitutive equations

International Nuclear Information System (INIS)

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

1977-01-01

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

Regression Models For Multivariate Count Data.

Science.gov (United States)

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

Nonparametric Mixture of Regression Models.

Science.gov (United States)

Huang, Mian; Li, Runze; Wang, Shaoli

2013-07-01

Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.

Gaussian Process Regression Model in Spatial Logistic Regression

Science.gov (United States)

Sofro, A.; Oktaviarina, A.

2018-01-01

Spatial analysis has developed very quickly in the last decade. One of the favorite approaches is based on the neighbourhood of the region. Unfortunately, there are some limitations such as difficulty in prediction. Therefore, we offer Gaussian process regression (GPR) to accommodate the issue. In this paper, we will focus on spatial modeling with GPR for binomial data with logit link function. The performance of the model will be investigated. We will discuss the inference of how to estimate the parameters and hyper-parameters and to predict as well. Furthermore, simulation studies will be explained in the last section.

Regression and regression analysis time series prediction modeling on climate data of quetta, pakistan

International Nuclear Information System (INIS)

Jafri, Y.Z.; Kamal, L.

2007-01-01

Various statistical techniques was used on five-year data from 1998-2002 of average humidity, rainfall, maximum and minimum temperatures, respectively. The relationships to regression analysis time series (RATS) were developed for determining the overall trend of these climate parameters on the basis of which forecast models can be corrected and modified. We computed the coefficient of determination as a measure of goodness of fit, to our polynomial regression analysis time series (PRATS). The correlation to multiple linear regression (MLR) and multiple linear regression analysis time series (MLRATS) were also developed for deciphering the interdependence of weather parameters. Spearman's rand correlation and Goldfeld-Quandt test were used to check the uniformity or non-uniformity of variances in our fit to polynomial regression (PR). The Breusch-Pagan test was applied to MLR and MLRATS, respectively which yielded homoscedasticity. We also employed Bartlett's test for homogeneity of variances on a five-year data of rainfall and humidity, respectively which showed that the variances in rainfall data were not homogenous while in case of humidity, were homogenous. Our results on regression and regression analysis time series show the best fit to prediction modeling on climatic data of Quetta, Pakistan. (author)

Robust mislabel logistic regression without modeling mislabel probabilities.

Science.gov (United States)

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

2018-03-01

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

Mixed Frequency Data Sampling Regression Models: The R Package midasr

Directory of Open Access Journals (Sweden)

Eric Ghysels

2016-08-01

Full Text Available When modeling economic relationships it is increasingly common to encounter data sampled at different frequencies. We introduce the R package midasr which enables estimating regression models with variables sampled at different frequencies within a MIDAS regression framework put forward in work by Ghysels, Santa-Clara, and Valkanov (2002. In this article we define a general autoregressive MIDAS regression model with multiple variables of different frequencies and show how it can be specified using the familiar R formula interface and estimated using various optimization methods chosen by the researcher. We discuss how to check the validity of the estimated model both in terms of numerical convergence and statistical adequacy of a chosen regression specification, how to perform model selection based on a information criterion, how to assess forecasting accuracy of the MIDAS regression model and how to obtain a forecast aggregation of different MIDAS regression models. We illustrate the capabilities of the package with a simulated MIDAS regression model and give two empirical examples of application of MIDAS regression.

Impact of multicollinearity on small sample hydrologic regression models

Science.gov (United States)

Kroll, Charles N.; Song, Peter

2013-06-01

Often hydrologic regression models are developed with ordinary least squares (OLS) procedures. The use of OLS with highly correlated explanatory variables produces multicollinearity, which creates highly sensitive parameter estimators with inflated variances and improper model selection. It is not clear how to best address multicollinearity in hydrologic regression models. Here a Monte Carlo simulation is developed to compare four techniques to address multicollinearity: OLS, OLS with variance inflation factor screening (VIF), principal component regression (PCR), and partial least squares regression (PLS). The performance of these four techniques was observed for varying sample sizes, correlation coefficients between the explanatory variables, and model error variances consistent with hydrologic regional regression models. The negative effects of multicollinearity are magnified at smaller sample sizes, higher correlations between the variables, and larger model error variances (smaller R2). The Monte Carlo simulation indicates that if the true model is known, multicollinearity is present, and the estimation and statistical testing of regression parameters are of interest, then PCR or PLS should be employed. If the model is unknown, or if the interest is solely on model predictions, is it recommended that OLS be employed since using more complicated techniques did not produce any improvement in model performance. A leave-one-out cross-validation case study was also performed using low-streamflow data sets from the eastern United States. Results indicate that OLS with stepwise selection generally produces models across study regions with varying levels of multicollinearity that are as good as biased regression techniques such as PCR and PLS.

Applied Regression Modeling A Business Approach

CERN Document Server

Pardoe, Iain

2012-01-01

An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a

Testing homogeneity in Weibull-regression models.

Science.gov (United States)

Bolfarine, Heleno; Valença, Dione M

2005-10-01

In survival studies with families or geographical units it may be of interest testing whether such groups are homogeneous for given explanatory variables. In this paper we consider score type tests for group homogeneity based on a mixing model in which the group effect is modelled as a random variable. As opposed to hazard-based frailty models, this model presents survival times that conditioned on the random effect, has an accelerated failure time representation. The test statistics requires only estimation of the conventional regression model without the random effect and does not require specifying the distribution of the random effect. The tests are derived for a Weibull regression model and in the uncensored situation, a closed form is obtained for the test statistic. A simulation study is used for comparing the power of the tests. The proposed tests are applied to real data sets with censored data.

Model-based Quantile Regression for Discrete Data

KAUST Repository

Padellini, Tullia; Rue, Haavard

2018-01-01

Quantile regression is a class of methods voted to the modelling of conditional quantiles. In a Bayesian framework quantile regression has typically been carried out exploiting the Asymmetric Laplace Distribution as a working likelihood. Despite

Detection of epistatic effects with logic regression and a classical linear regression model.

Science.gov (United States)

Malina, Magdalena; Ickstadt, Katja; Schwender, Holger; Posch, Martin; Bogdan, Małgorzata

2014-02-01

To locate multiple interacting quantitative trait loci (QTL) influencing a trait of interest within experimental populations, usually methods as the Cockerham's model are applied. Within this framework, interactions are understood as the part of the joined effect of several genes which cannot be explained as the sum of their additive effects. However, if a change in the phenotype (as disease) is caused by Boolean combinations of genotypes of several QTLs, this Cockerham's approach is often not capable to identify them properly. To detect such interactions more efficiently, we propose a logic regression framework. Even though with the logic regression approach a larger number of models has to be considered (requiring more stringent multiple testing correction) the efficient representation of higher order logic interactions in logic regression models leads to a significant increase of power to detect such interactions as compared to a Cockerham's approach. The increase in power is demonstrated analytically for a simple two-way interaction model and illustrated in more complex settings with simulation study and real data analysis.

Privacy-Preserving Distributed Linear Regression on High-Dimensional Data

Directory of Open Access Journals (Sweden)

Gascón Adrià

2017-10-01

Full Text Available We propose privacy-preserving protocols for computing linear regression models, in the setting where the training dataset is vertically distributed among several parties. Our main contribution is a hybrid multi-party computation protocol that combines Yao’s garbled circuits with tailored protocols for computing inner products. Like many machine learning tasks, building a linear regression model involves solving a system of linear equations. We conduct a comprehensive evaluation and comparison of different techniques for securely performing this task, including a new Conjugate Gradient Descent (CGD algorithm. This algorithm is suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Our technique improves on Nikolaenko et al.’s method for privacy-preserving ridge regression (S&P 2013, and can be used as a building block in other analyses. We implement a complete system and demonstrate that our approach is highly scalable, solving data analysis problems with one million records and one hundred features in less than one hour of total running time.

Migrating Toward Fully 4-D Geodynamical Models of Asthenospheric Circulation and Melt Production at Mid-Ocean Ridges

Science.gov (United States)

van Dam, L.; Kincaid, C. R.; Pockalny, R. A.; Sylvia, R. T.; Hall, P. S.

2017-12-01

Lateral migration of mid-ocean ridge spreading centers is a well-documented phenomenon leading to asymmetric melt production and the surficial expressions thereof. This form of plate motion has been difficult to incorporate into both numerical and analogue geodynamical models, and consequently, current estimates of time-dependent flow, material transport, and melting in the mantle beneath ridges are lacking. To address this, we have designed and built an innovative research apparatus that allows for precise and repeatable simulations of mid-ocean ridge spreading and migration. Three pairs of counter-rotating belts with adjustable lateral orientations are scaled to simulate spreading at, and flow beneath, three 600km wide ridge segments with up to 300km transform offsets. This apparatus is attached to a drive system that allows us to test a full range of axis-parallel to axis-normal migration directions, and is suspended above a reservoir of viscous glucose syrup, a scaled analogue for the upper mantle, and neutrally buoyant tracers. We image plate-driven flow in the syrup with high-resolution digital cameras and use particle image velocimetry methods to obtain information about transport pathlines and flow-induced anisotropy. Suites of experiments are run with and without ridge migration to determine the overall significance of migration on spatial and temporal characteristics of shallow mantle flow. Our experiments cover an expansive parameter space by including various spreading rates, migration speeds and directions, degrees of spreading asymmetry, transform-offset lengths, and upper mantle viscosity conditions. Preliminary results highlight the importance of modeling migratory plate forces. Mantle material exhibits a significant degree of lateral transport, particularly between ridge segments and towards the melt triangle. Magma supply to the melting region is highly complex; parcels of material do not necessarily move along fixed streamlines, rather, they can

AN APPLICATION OF FUNCTIONAL MULTIVARIATE REGRESSION MODEL TO MULTICLASS CLASSIFICATION

OpenAIRE

Krzyśko, Mirosław; Smaga, Łukasz

2017-01-01

In this paper, the scale response functional multivariate regression model is considered. By using the basis functions representation of functional predictors and regression coefficients, this model is rewritten as a multivariate regression model. This representation of the functional multivariate regression model is used for multiclass classification for multivariate functional data. Computational experiments performed on real labelled data sets demonstrate the effectiveness of the proposed ...

Regularized principal covariates regression and its application to finding coupled patterns in climate fields

Science.gov (United States)

Fischer, M. J.

2014-02-01

There are many different methods for investigating the coupling between two climate fields, which are all based on the multivariate regression model. Each different method of solving the multivariate model has its own attractive characteristics, but often the suitability of a particular method for a particular problem is not clear. Continuum regression methods search the solution space between the conventional methods and thus can find regression model subspaces that mix the attractive characteristics of the end-member subspaces. Principal covariates regression is a continuum regression method that is easily applied to climate fields and makes use of two end-members: principal components regression and redundancy analysis. In this study, principal covariates regression is extended to additionally span a third end-member (partial least squares or maximum covariance analysis). The new method, regularized principal covariates regression, has several attractive features including the following: it easily applies to problems in which the response field has missing values or is temporally sparse, it explores a wide range of model spaces, and it seeks a model subspace that will, for a set number of components, have a predictive skill that is the same or better than conventional regression methods. The new method is illustrated by applying it to the problem of predicting the southern Australian winter rainfall anomaly field using the regional atmospheric pressure anomaly field. Regularized principal covariates regression identifies four major coupled patterns in these two fields. The two leading patterns, which explain over half the variance in the rainfall field, are related to the subtropical ridge and features of the zonally asymmetric circulation.

Dynamical instability produces transform faults at mid-ocean ridges.

Science.gov (United States)

Gerya, Taras

2010-08-27

Transform faults at mid-ocean ridges--one of the most striking, yet enigmatic features of terrestrial plate tectonics--are considered to be the inherited product of preexisting fault structures. Ridge offsets along these faults therefore should remain constant with time. Here, numerical models suggest that transform faults are actively developing and result from dynamical instability of constructive plate boundaries, irrespective of previous structure. Boundary instability from asymmetric plate growth can spontaneously start in alternate directions along successive ridge sections; the resultant curved ridges become transform faults within a few million years. Fracture-related rheological weakening stabilizes ridge-parallel detachment faults. Offsets along the transform faults change continuously with time by asymmetric plate growth and discontinuously by ridge jumps.

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

Science.gov (United States)

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

2017-06-01

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

A regression approach for zircaloy-2 in-reactor creep constitutive equations

International Nuclear Information System (INIS)

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

1977-01-01

In this paper the methodology of multiple regressions as applied to zircaloy-2 in-reactor creep data analysis and construction of constitutive equation are illustrated. While the resulting constitutive equation can be used in creep analysis of in-reactor zircaloy structural components, the methodology itself is entirely general and can be applied to any creep data analysis. From data analysis and model development point of views, both the assumption of independence and prior committment to specific model forms are unacceptable. One would desire means which can not only estimate the required parameters directly from data but also provide basis for model selections, viz., one model against others. Basic understanding of the physics of deformation is important in choosing the forms of starting physical model equations, but the justifications must rely on their abilities in correlating the overall data. The promising aspects of multiple regression creep data analysis are briefly outlined as follows: (1) when there are more than one variable involved, there is no need to make the assumption that each variable affects the response independently. No separate normalizations are required either and the estimation of parameters is obtained by solving many simultaneous equations. The number of simultaneous equations is equal to the number of data sets, (2) regression statistics such as R 2 - and F-statistics provide measures of the significance of regression creep equation in correlating the overall data. The relative weights of each variable on the response can also be obtained. (3) Special regression techniques such as step-wise, ridge, and robust regressions and residual plots, etc., provide diagnostic tools for model selections

Environmental baseline survey report for West Black Oak Ridge, East Black Oak Ridge, McKinney Ridge, West Pine Ridge and parcel 21D in the vicinity of the East Technology Park, Oak Ridge, Tennessee

Energy Technology Data Exchange (ETDEWEB)

King, David A. [Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, TN (United States). Independent Environmental Assessment and Verification Program

2012-11-29

This environmental baseline survey (EBS) report documents the baseline environmental conditions of five land parcels located near the U.S. Department of Energy?s (DOE?s) East Tennessee Technology Park (ETTP), including West Black Oak Ridge, East Black Oak Ridge, McKinney Ridge, West Pine Ridge, and Parcel 21d. Preparation of this report included the detailed search of federal government records, title documents, aerial photos that may reflect prior uses, and visual inspections of the property and adjacent properties. Interviews with current employees involved in, or familiar with, operations on the real property were also conducted to identify any areas on the property where hazardous substances and petroleum products, or their derivatives, and acutely hazardous wastes may have been released or disposed. In addition, a search was made of reasonably obtainable federal, state, and local government records of each adjacent facility where there has been a release of any hazardous substance or any petroleum product or their derivatives, including aviation fuel and motor oil, and which is likely to cause or contribute to a release of any hazardous substance or any petroleum product or its derivatives, including aviation fuel or motor oil, on the real property. A radiological survey and soil/sediment sampling was conducted to assess baseline conditions of Parcel 21d that were not addressed by the soils-only no-further-investigation (NFI) reports. Groundwater sampling was also conducted to support a Parcel 21d decision. Based on available data West Black Oak Ridge, East Black Oak Ridge, McKinney Ridge, and West Pine Ridge are not impacted by site operations and are not subject to actions per the Federal Facility Agreement (FFA). This determination is supported by visual inspections, records searches and interviews, groundwater conceptual modeling, approved NFI reports, analytical data, and risk analysis results. Parcel 21d data, however, demonstrate impacts from site

Environmental baseline survey report for West Black Oak Ridge, East Black Oak Ridge, McKinney Ridge, West Pine Ridge and parcel 21D in the vicinity of the East Technology Park, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

King, David A.

2012-01-01

This environmental baseline survey (EBS) report documents the baseline environmental conditions of five land parcels located near the U.S. Department of Energy's (DOE's) East Tennessee Technology Park (ETTP), including West Black Oak Ridge, East Black Oak Ridge, McKinney Ridge, West Pine Ridge, and Parcel 21d. Preparation of this report included the detailed search of federal government records, title documents, aerial photos that may reflect prior uses, and visual inspections of the property and adjacent properties. Interviews with current employees involved in, or familiar with, operations on the real property were also conducted to identify any areas on the property where hazardous substances and petroleum products, or their derivatives, and acutely hazardous wastes may have been released or disposed. In addition, a search was made of reasonably obtainable federal, state, and local government records of each adjacent facility where there has been a release of any hazardous substance or any petroleum product or their derivatives, including aviation fuel and motor oil, and which is likely to cause or contribute to a release of any hazardous substance or any petroleum product or its derivatives, including aviation fuel or motor oil, on the real property. A radiological survey and soil/sediment sampling was conducted to assess baseline conditions of Parcel 21d that were not addressed by the soils-only no-further-investigation (NFI) reports. Groundwater sampling was also conducted to support a Parcel 21d decision. Based on available data West Black Oak Ridge, East Black Oak Ridge, McKinney Ridge, and West Pine Ridge are not impacted by site operations and are not subject to actions per the Federal Facility Agreement (FFA). This determination is supported by visual inspections, records searches and interviews, groundwater conceptual modeling, approved NFI reports, analytical data, and risk analysis results. Parcel 21d data, however, demonstrate impacts from site

Two-Dimensional Heat Transfer Modeling of the Formosa Ridge Offshore SW Taiwan: Implication for Fluid Migrating Paths of a Cold Seep Site

Science.gov (United States)

Tsai, Y.; Chi, W.; Liu, C.; Shyu, C.

2011-12-01

The Formosa Ridge, a small ridge located on the passive China continental slope offshore southwestern Taiwan, is an active cold seep site. Large and dense chemosynthetic communities were found there by the ROV Hyper-Dolphin during the 2007 NT0705 cruise. A vertical blank zone is clearly observed on all the seismic profiles across the cold seep site. This narrow zone is interpreted to be the fluid conduit of the seep site. Previous studies suggest that cold sea water carrying large amount of sulfate could flow into the fluid system from flanks of the ridge, and forms a very effective fluid circulation system that emits both methane and hydrogen sulfide to feed the unusual chemosynthetic communities observed at the Formosa Ridge cold seep site. Here we use thermal signals to study possible fluid flow migration paths. In 2008 and 2010, we have collected vdense thermal probe data at this site. We also study the temperatures at Bottom-Simulating Reflectors (BSRs) based on methane hydrate phase diagram. We perform 2D finite element thermal conductive simulations to study the effects of bathymetry on the temperature field in the ridge, and compare the simulation result with thermal probe and BSR-derived datasets. The boundary conditions include insulated boundaries on both sides, and we assign a fix temperature at the bottom of the model using an average regional geothermal gradient. Sensitivity tests and thermal probe data from a nearby region give a regional background geothermal gradient of 0.04 to 0.05 °C/m. The outputs of the simulation runs include geothermal gradient and temperature at different parts of the model. The model can fit the geothermal gradient at a distance away from the ridge where there is less geophysics evidence of fluid flow. However our model over-predicts the geothermal gradient by 50% at the ridge top. We also compare simulated temperature field and found that under the flanks of the ridge the temperature is cooled by 2 °C compared with the

Alternative regression models to assess increase in childhood BMI.

Science.gov (United States)

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

2008-09-08

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

Volcanism and hydrothermalism on a hotspot-influenced ridge: Comparing Reykjanes Peninsula and Reykjanes Ridge, Iceland

Science.gov (United States)

Pałgan, Dominik; Devey, Colin W.; Yeo, Isobel A.

2017-12-01

Current estimates indicate that the number of high-temperature vents (one of the primary pathways for the heat extraction from the Earth's mantle) - at least 1 per 100 km of axial length - scales with spreading rate and should scale with crustal thickness. But up to present, shallow ridge axes underlain by thick crust show anomalously low incidences of high-temperature activity. Here we compare the Reykjanes Ridge, an abnormally shallow ridge with thick crust and only one high-temperature vent known over 900 km axial length, to the adjacent subaerial Reykjanes Peninsula (RP), which is characterized by high-temperature geothermal sites confined to four volcanic systems transected by fissure swarms with young (Holocene) volcanic activity, multiple faults, cracks and fissures, and continuous seismic activity. New high-resolution bathymetry (gridded at 60 m) of the Reykjanes Ridge between 62°30‧N and 63°30‧N shows seven Axial Volcanic Ridges (AVR) that, based on their morphology, geometry and tectonic regime, are analogues for the volcanic systems and fissure swarms on land. We investigate in detail the volcano-tectonic features of all mapped AVRs and show that they do not fit with the previously suggested 4-stage evolution model for AVR construction. Instead, we suggest that AVR morphology reflects the robust or weak melt supply to the system and two (or more) eruption mechanisms may co-exist on one AVR (in contrast to 4-stage evolution model). Our interpretations indicate that, unlike on the Reykjanes Peninsula, faults on and around AVRs do not cluster in orientation domains but all are subparallel to the overall strike of AVRs (orthogonal to spreading direction). High abundance of seamounts shows that the region centered at 62°47‧N and 25°04‧W (between AVR-5 and -6) is volcanically robust while the highest fault density implies that AVR-1 and southern part of AVR-6 rather undergo period of melt starvation. Based on our observations and interpretations we

Alternative regression models to assess increase in childhood BMI

OpenAIRE

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

2008-01-01

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 childre...

A comparative study of some robust ridge and liu estimators ...

African Journals Online (AJOL)

In multiple linear regression analysis, multicollinearity and outliers are two main problems. When multicollinearity exists, biased estimation techniques such as Ridge and Liu Estimators are preferable to Ordinary Least Square. On the other hand, when outliers exist in the data, robust estimators like M, MM, LTS and S ...

Patch behaviour and predictability properties of modelled finite-amplitude sand ridges on the inner shelf

NARCIS (Netherlands)

Vis-star, N.C.; de Swart, H.E.; Calvete, D.

2008-01-01

The long-term evolution of shoreface-connected sand ridges is investigated with a nonlinear spectral model which governs the dynamics of waves, currents, sediment transport and the bed level on the inner shelf. Wave variables are calculated with a shoaling-refraction model instead of using a

Thermal Efficiency Degradation Diagnosis Method Using Regression Model

International Nuclear Information System (INIS)

Jee, Chang Hyun; Heo, Gyun Young; Jang, Seok Won; Lee, In Cheol

2011-01-01

This paper proposes an idea for thermal efficiency degradation diagnosis in turbine cycles, which is based on turbine cycle simulation under abnormal conditions and a linear regression model. The correlation between the inputs for representing degradation conditions (normally unmeasured but intrinsic states) and the simulation outputs (normally measured but superficial states) was analyzed with the linear regression model. The regression models can inversely response an associated intrinsic state for a superficial state observed from a power plant. The diagnosis method proposed herein is classified into three processes, 1) simulations for degradation conditions to get measured states (referred as what-if method), 2) development of the linear model correlating intrinsic and superficial states, and 3) determination of an intrinsic state using the superficial states of current plant and the linear regression model (referred as inverse what-if method). The what-if method is to generate the outputs for the inputs including various root causes and/or boundary conditions whereas the inverse what-if method is the process of calculating the inverse matrix with the given superficial states, that is, component degradation modes. The method suggested in this paper was validated using the turbine cycle model for an operating power plant

Random regression models for detection of gene by environment interaction

Directory of Open Access Journals (Sweden)

Meuwissen Theo HE

2007-02-01

Full Text Available Abstract Two random regression models, where the effect of a putative QTL was regressed on an environmental gradient, are described. The first model estimates the correlation between intercept and slope of the random regression, while the other model restricts this correlation to 1 or -1, which is expected under a bi-allelic QTL model. The random regression models were compared to a model assuming no gene by environment interactions. The comparison was done with regards to the models ability to detect QTL, to position them accurately and to detect possible QTL by environment interactions. A simulation study based on a granddaughter design was conducted, and QTL were assumed, either by assigning an effect independent of the environment or as a linear function of a simulated environmental gradient. It was concluded that the random regression models were suitable for detection of QTL effects, in the presence and absence of interactions with environmental gradients. Fixing the correlation between intercept and slope of the random regression had a positive effect on power when the QTL effects re-ranked between environments.

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.

The microcomputer scientific software series 2: general linear model--regression.

Science.gov (United States)

Harold M. Rauscher

1983-01-01

The general linear model regression (GLMR) program provides the microcomputer user with a sophisticated regression analysis capability. The output provides a regression ANOVA table, estimators of the regression model coefficients, their confidence intervals, confidence intervals around the predicted Y-values, residuals for plotting, a check for multicollinearity, a...

Wavelet regression model in forecasting crude oil price

Science.gov (United States)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

Predicting and Modelling of Survival Data when Cox's Regression Model does not hold

DEFF Research Database (Denmark)

Scheike, Thomas H.; Zhang, Mei-Jie

2002-01-01

Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects......Aalen model; additive risk model; counting processes; competing risk; Cox regression; flexible modeling; goodness of fit; prediction of survival; survival analysis; time-varying effects...

Spatial stochastic regression modelling of urban land use

International Nuclear Information System (INIS)

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

2014-01-01

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

Physics constrained nonlinear regression models for time series

International Nuclear Information System (INIS)

Majda, Andrew J; Harlim, John

2013-01-01

A central issue in contemporary science is the development of data driven statistical nonlinear dynamical models for time series of partial observations of nature or a complex physical model. It has been established recently that ad hoc quadratic multi-level regression (MLR) models can have finite-time blow up of statistical solutions and/or pathological behaviour of their invariant measure. Here a new class of physics constrained multi-level quadratic regression models are introduced, analysed and applied to build reduced stochastic models from data of nonlinear systems. These models have the advantages of incorporating memory effects in time as well as the nonlinear noise from energy conserving nonlinear interactions. The mathematical guidelines for the performance and behaviour of these physics constrained MLR models as well as filtering algorithms for their implementation are developed here. Data driven applications of these new multi-level nonlinear regression models are developed for test models involving a nonlinear oscillator with memory effects and the difficult test case of the truncated Burgers–Hopf model. These new physics constrained quadratic MLR models are proposed here as process models for Bayesian estimation through Markov chain Monte Carlo algorithms of low frequency behaviour in complex physical data. (paper)

Logistic regression modelling: procedures and pitfalls in developing and interpreting prediction models

Directory of Open Access Journals (Sweden)

Nataša Šarlija

2017-01-01

Full Text Available This study sheds light on the most common issues related to applying logistic regression in prediction models for company growth. The purpose of the paper is 1 to provide a detailed demonstration of the steps in developing a growth prediction model based on logistic regression analysis, 2 to discuss common pitfalls and methodological errors in developing a model, and 3 to provide solutions and possible ways of overcoming these issues. Special attention is devoted to the question of satisfying logistic regression assumptions, selecting and defining dependent and independent variables, using classification tables and ROC curves, for reporting model strength, interpreting odds ratios as effect measures and evaluating performance of the prediction model. Development of a logistic regression model in this paper focuses on a prediction model of company growth. The analysis is based on predominantly financial data from a sample of 1471 small and medium-sized Croatian companies active between 2009 and 2014. The financial data is presented in the form of financial ratios divided into nine main groups depicting following areas of business: liquidity, leverage, activity, profitability, research and development, investing and export. The growth prediction model indicates aspects of a business critical for achieving high growth. In that respect, the contribution of this paper is twofold. First, methodological, in terms of pointing out pitfalls and potential solutions in logistic regression modelling, and secondly, theoretical, in terms of identifying factors responsible for high growth of small and medium-sized companies.

The ESASSI-08 cruise in the South Scotia Ridge region: An inverse model property-transport analysis over the Ridge

Science.gov (United States)

Palmer, Margarita; Gomis, Damià; Del Mar Flexas, Maria; Jordà, Gabriel; Naveira-Garabato, Alberto; Jullion, Loic; Tsubouchi, Takamasa

2010-05-01

The ESASSI-08 oceanographic cruise carried out in January 2008 was the most significant milestone of the ESASSI project. ESASSI is the Spanish component of the Synoptic Antarctic Shelf-Slope Interactions (SASSI) study, one of the core projects of the International Polar Year. Hydrographical and biochemical (oxygen, CFCs, nutrients, chlorophyll content, alkalinity, pH, DOC) data were obtained along 11 sections in the South Scotia Ridge (SSR) region, between Elephant and South Orkney Islands. One of the aims of the ESASSI project is to determine the northward outflow of cold and ventilated waters from the Weddell Sea into the Scotia Sea. For that purpose, the accurate estimation of mass, heat, salt, and oxygen transport over the Ridge is requested. An initial analysis of transports across the different sections was first obtained from CTD and ADCP data. The following step has been the application of an inverse method, in order to obtain a better estimation of the net flow for the different water masses present in the region. The set of property-conservation equations considered by the inverse model includes mass, heat and salinity fluxes. The "box" is delimited by the sections along the northern flank of the SSR, between Elephant Island and 50°W, the southern flank of the Ridge, between 51.5°W and 50°W, the 50°W meridian and a diagonal line between Elephant Island and 51.5°W, 61.75°S. Results show that the initial calculations of transports suffered of a significant volume imbalance, due to the inherent errors of ship-ADCP data, the complicated topography and the presence of strong tidal currents in some sections. We present the post-inversion property transports across the rim of the box (and their error bars) for the different water masses.

Regression Models for Repairable Systems

Czech Academy of Sciences Publication Activity Database

Novák, Petr

2015-01-01

Roč. 17, č. 4 (2015), s. 963-972 ISSN 1387-5841 Institutional support: RVO:67985556 Keywords : Reliability analysis * Repair models * Regression Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.782, year: 2015 http://library.utia.cas.cz/separaty/2015/SI/novak-0450902.pdf

Remedial Investigation Work Plan for Chestnut Ridge Operable Unit 1 (Chestnut Ridge Security Pits) at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1994-03-01

This document outlines the activities necessary to conduct a Remedial Investigation (RI) of the Chestnut Ridge Security Pits (CRSP) at the Oak Ridge Y-12 Plant. The CRSP, also designated Chestnut Ridge Operable Unit (OU) 1, is one of four OUs along Chestnut Ridge on the Oak Ridge Reservation (ORR). The purpose of the RI is to collect data to (1) evaluate the nature and extent of known and suspected contaminants, (2) support an Ecological Risk Assessment (ERA) and a Human Health Risk Assessment (HHRA), (3) support the feasibility study in the development and analysis of remedial alternatives, and (4) ultimately, develop a Record of Decision (ROD) for the site. This chapter summarizes the regulatory background of environmental investigation on the ORR and the approach currently being followed and provides an overview of the RI to be conducted at the CRSP. Subsequent chapters provide details on site history, sampling activities, procedures and methods, quality assurance (QA), health and safety, and waste management related to the RI

Remedial Investigation Work Plan for Chestnut Ridge Operable Unit 1 (Chestnut Ridge Security Pits) at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

Energy Technology Data Exchange (ETDEWEB)

1994-03-01

This document outlines the activities necessary to conduct a Remedial Investigation (RI) of the Chestnut Ridge Security Pits (CRSP) at the Oak Ridge Y-12 Plant. The CRSP, also designated Chestnut Ridge Operable Unit (OU) 1, is one of four OUs along Chestnut Ridge on the Oak Ridge Reservation (ORR). The purpose of the RI is to collect data to (1) evaluate the nature and extent of known and suspected contaminants, (2) support an Ecological Risk Assessment (ERA) and a Human Health Risk Assessment (HHRA), (3) support the feasibility study in the development and analysis of remedial alternatives, and (4) ultimately, develop a Record of Decision (ROD) for the site. This chapter summarizes the regulatory background of environmental investigation on the ORR and the approach currently being followed and provides an overview of the RI to be conducted at the CRSP. Subsequent chapters provide details on site history, sampling activities, procedures and methods, quality assurance (QA), health and safety, and waste management related to the RI.

An aerial radiological survey of the Oak Ridge Reservation, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

Maurer, R.J.

1993-04-01

An aerial radiological survey of the Oak Ridge Reservation (ORR) and surrounding area in Oak Ridge, Tennessee, was conducted during the period March 30 to April 14,1992. The purpose of the survey was to measure and document the terrestrial radiological environment of the Oak Ridge Reservation for use in environmental management programs and emergency response planning. The aerial survey was flown at an altitude of 150 feet (46 meters) along a series of parallel lines 250 feet (76 meters) apart and included X-10 (Oak Ridge National Laboratory), K-25 (former Gaseous Diffusion Plant), Y-12 (Weapons Production Plant), the Freels Bend Area and Oak Ridge Institute for Science and Education, the East Fork Poplar Creek (100-year floodplain extending from K-25 to Y-12), Elza Gate (former uranium ore storage site located in the city of Oak Ridge), Parcel A, the Clinch River (river banks extending from Melton Hill Dam to the city of Kingston), and the CSX Railroad Tracks (extending from Y-12 to the city of Oak Ridge). The survey encompassed approximately 55 square miles (1 41 square kilometers) of the Oak Ridge Reservation and surrounding area

Petrological systematics of mid-ocean ridge basalts: Constraints on melt generation beneath ocean ridges

Science.gov (United States)

Langmuir, Charles H.; Klein, Emily M.; Plank, Terry

ridges, and still more enriched basalts can erupt sporadically along the entire length of the EPR. This leads to very different histograms of distribution for the data sets as a whole, and a very different distribution of chemistry along strike for the two ridges. Despite these differences, the mean Ce/Sm ratios from the two ridges are identical. Existing methods for calculating the major element compositions of mantle melts [Klein and Langmuir, 1987; McKenzie and Bickle, 1988; Niu and Batiza, 1991] are critically examined. New quantitative methods for mantle melting and high pressure fractionation are developed to evaluate the chemical consequences of melting and fractionation processes and mantle heterogeneity. The new methods rely on new equations for partition coefficients for the major elements between mantle minerals and melts. The melting calculations can be used to investigate the chemical compositions produced by small extents of melting or high pressures of melting that cannot yet be determined experimentally. Application of the new models to the observations described above leads to two major conclusions: (1) The global correlations for normal ridges are caused by variations in mantle temperature, as suggested by Klein and Langmuir [1987] and not by mantle heterogeneity. (2) Local variations are caused by melting processes, but are not yet quantitatively accounted for. On slower spreading ridges, local variations are controlled by the melting regime in the mantle. On the EPR, local variations are predominantly controlled by ubiquitous, small scale heterogeneites. Volatile content may be an important and as yet undetermined factor in affecting the observed variations in major elements. We propose a hypothesis, similar to one proposed by Allegre et al [1984] for isotopic data, to explain the differences between the Atlantic and Pacific local trends, and the trace element systematics of the two ocean basins, as consequences of spreading rate and a different

Ridge, Bulk, and Medium Response: How to Kill Models and Learn Something in the Process

International Nuclear Information System (INIS)

Nagle, J.L.

2009-01-01

In these proceedings, we highlight experimental data (published and preliminary) related to jet quenching and the response of the medium to this deposited energy. Signatures in two- and three-particle hadron correlations indicate interesting structures near the trigger particle in azimuth and over a broad range in pseudo-rapidity, often termed 'the ridge', and conical-like structures separated in azimuth opposite to the trigger particle. We review numerous theoretical interpretations of the ridge in particular with a critical eye for the key properties that allow one to discriminate between, or rule out, certain physical pictures and models (and hopefully learn something in the process).

Geographically weighted regression model on poverty indicator

Science.gov (United States)

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

2017-12-01

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

Mantle Convection beneath the Aegir Ridge, a Shadow in the Iceland Hotspot

Science.gov (United States)

Howell, S. M.; Ito, G.; Breivik, A. J.; Hanan, B. B.; Mjelde, R.; Sayit, K.; Vogt, P. R.

2012-12-01

The Iceland Hotspot has produced extensive volcanism spanning much of the ocean basin between Greenland and Norway, forming one of the world's largest igneous provinces. However, an apparent igneous "shadow" in hotspot activity is located at the fossil Aegir Ridge, which formed anomalously thin crust, despite this ridge being near the Iceland hotspot when it was active. The Aegir Ridge accommodated seafloor spreading northeast of present-day Iceland from the time of continental breakup at ~55 Ma until ~25 Ma, at which point spreading shifted west to the Kolbeinsey Ridge. To address the cause of the anomalously thin crust produced by the Aegir Ridge, we use three-dimensional numerical models to simulate the interaction between a mantle plume beneath the Iceland hotspot, rifting continental lithosphere, and the time-evolving North Atlantic ridge system. Two end-member hypotheses were investigated: (1) Material emanating from the Iceland mantle plume was blocked from reaching the Aegir Ridge by the thick lithosphere of the Jan Mayen Microcontinent as the Kolbeinsey Ridge began rifting it from Greenland at ~30 Ma, just east of the plume center; (2) Plume material was not blocked and did reach the Aegir Ridge, but had already experienced partial melting closer to the hotspot. This material was then unable to produce melt volumes at the Aegir Ridge comparable to those of pristine mantle. To test these hypotheses, we vary the volume flux and viscosity of the plume, and identify which conditions do and do not lead to the Aegir Ridge forming anomalously thin crust. Results show that the combination of plume material being drawn into the lithospheric channels beneath the Reykjanes Ridge and Kolbeinsey Ridge after their respective openings, and the impedance of plume flow by the Jan Mayen Microcontinent (hypothesis 1), can deprive the Aegir Ridge of plume influence. This leads to low crustal thicknesses that are comparable to those observed. We have yet to produce a model

On a Robust MaxEnt Process Regression Model with Sample-Selection

Directory of Open Access Journals (Sweden)

Hea-Jung Kim

2018-04-01

Full Text Available In a regression analysis, a sample-selection bias arises when a dependent variable is partially observed as a result of the sample selection. This study introduces a Maximum Entropy (MaxEnt process regression model that assumes a MaxEnt prior distribution for its nonparametric regression function and finds that the MaxEnt process regression model includes the well-known Gaussian process regression (GPR model as a special case. Then, this special MaxEnt process regression model, i.e., the GPR model, is generalized to obtain a robust sample-selection Gaussian process regression (RSGPR model that deals with non-normal data in the sample selection. Various properties of the RSGPR model are established, including the stochastic representation, distributional hierarchy, and magnitude of the sample-selection bias. These properties are used in the paper to develop a hierarchical Bayesian methodology to estimate the model. This involves a simple and computationally feasible Markov chain Monte Carlo algorithm that avoids analytical or numerical derivatives of the log-likelihood function of the model. The performance of the RSGPR model in terms of the sample-selection bias correction, robustness to non-normality, and prediction, is demonstrated through results in simulations that attest to its good finite-sample performance.

On concurvity in nonlinear and nonparametric regression models

Directory of Open Access Journals (Sweden)

Sonia Amodio

2014-12-01

Full Text Available When data are affected by multicollinearity in the linear regression framework, then concurvity will be present in fitting a generalized additive model (GAM. The term concurvity describes nonlinear dependencies among the predictor variables. As collinearity results in inflated variance of the estimated regression coefficients in the linear regression model, the result of the presence of concurvity leads to instability of the estimated coefficients in GAMs. Even if the backfitting algorithm will always converge to a solution, in case of concurvity the final solution of the backfitting procedure in fitting a GAM is influenced by the starting functions. While exact concurvity is highly unlikely, approximate concurvity, the analogue of multicollinearity, is of practical concern as it can lead to upwardly biased estimates of the parameters and to underestimation of their standard errors, increasing the risk of committing type I error. We compare the existing approaches to detect concurvity, pointing out their advantages and drawbacks, using simulated and real data sets. As a result, this paper will provide a general criterion to detect concurvity in nonlinear and non parametric regression models.

Semiparametric Mixtures of Regressions with Single-index for Model Based Clustering

OpenAIRE

Xiang, Sijia; Yao, Weixin

2017-01-01

In this article, we propose two classes of semiparametric mixture regression models with single-index for model based clustering. Unlike many semiparametric/nonparametric mixture regression models that can only be applied to low dimensional predictors, the new semiparametric models can easily incorporate high dimensional predictors into the nonparametric components. The proposed models are very general, and many of the recently proposed semiparametric/nonparametric mixture regression models a...

Short-term electricity prices forecasting based on support vector regression and Auto-regressive integrated moving average modeling

International Nuclear Information System (INIS)

Che Jinxing; Wang Jianzhou

2010-01-01

In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.

A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover

Directory of Open Access Journals (Sweden)

Akpona Okujeni

2014-07-01

Full Text Available Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR, kernel ridge regression (KRR, artificial neural networks (NN, random forest regression (RFR and partial least squares regression (PLSR. Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, grass- and tree-covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN or limited (RFR and PLSR performances when comprehensively mapping urban land cover. Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying urban land cover from imaging spectrometer data at multiple scales.

Modeling oil production based on symbolic regression

International Nuclear Information System (INIS)

Yang, Guangfei; Li, Xianneng; Wang, Jianliang; Lian, Lian; Ma, Tieju

2015-01-01

Numerous models have been proposed to forecast the future trends of oil production and almost all of them are based on some predefined assumptions with various uncertainties. In this study, we propose a novel data-driven approach that uses symbolic regression to model oil production. We validate our approach on both synthetic and real data, and the results prove that symbolic regression could effectively identify the true models beneath the oil production data and also make reliable predictions. Symbolic regression indicates that world oil production will peak in 2021, which broadly agrees with other techniques used by researchers. Our results also show that the rate of decline after the peak is almost half the rate of increase before the peak, and it takes nearly 12 years to drop 4% from the peak. These predictions are more optimistic than those in several other reports, and the smoother decline will provide the world, especially the developing countries, with more time to orchestrate mitigation plans. -- Highlights: •A data-driven approach has been shown to be effective at modeling the oil production. •The Hubbert model could be discovered automatically from data. •The peak of world oil production is predicted to appear in 2021. •The decline rate after peak is half of the increase rate before peak. •Oil production projected to decline 4% post-peak

Genomic-Enabled Prediction Based on Molecular Markers and Pedigree Using the Bayesian Linear Regression Package in R

Directory of Open Access Journals (Sweden)

Paulino Pérez

2010-09-01

Full Text Available The availability of dense molecular markers has made possible the use of genomic selection in plant and animal breeding. However, models for genomic selection pose several computational and statistical challenges and require specialized computer programs, not always available to the end user and not implemented in standard statistical software yet. The R-package BLR (Bayesian Linear Regression implements several statistical procedures (e.g., Bayesian Ridge Regression, Bayesian LASSO in a unified framework that allows including marker genotypes and pedigree data jointly. This article describes the classes of models implemented in the BLR package and illustrates their use through examples. Some challenges faced when applying genomic-enabled selection, such as model choice, evaluation of predictive ability through cross-validation, and choice of hyper-parameters, are also addressed.

Wrinkle Ridges and Young Fresh Crater

Science.gov (United States)

2002-01-01

(Released 10 May 2002) The Science Wrinkle ridges are a very common landform on Mars, Mercury, Venus, and the Moon. These ridges are linear to arcuate asymmetric topographic highs commonly found on smooth plains. The origin of wrinkle ridges is not certain and two leading hypotheses have been put forth by scientists over the past 40 years. The volcanic model calls for the extrusion of high viscosity lavas along linear conduits. This thick lava accumulated over these conduits and formed the ridges. The other model is tectonic and advocates that the ridges are formed by compressional faulting and folding. Today's THEMIS image is of the ridged plains of Lunae Planum located between Kasei Valles and Valles Marineris in the northern hemisphere of the planet. Wrinkle ridges are found mostly along the eastern side of the image. The broadest wrinkle ridges in this image are up to 2 km wide. A 3 km diameter young fresh crater is located near the bottom of the image. The crater's ejecta blanket is also clearly seen surrounding the sharp well-defined crater rim. These features are indicative of a very young crater that has not been subjected to erosional processes. The Story The great thing about the solar system is that planets are both alike and different. They're all foreign enough to be mysterious and intriguing, and yet familiar enough to be seen as planetary 'cousins.' By comparing them, we can learn a lot about how planets form and then evolve geologically over time. Crinkled over smooth plains, the long, wavy raised landforms seen here are called 'wrinkle ridges,' and they've been found on Mars, Mercury, Venus, and the Moon - that is, on rocky bodies that are a part of our inner solar system. We know from this observation that planets (and large-enough moons) follow similar processes. What we don't know for sure is HOW these processes work. Scientists have been trying to understand how wrinkle ridges form for 40 years, and they still haven't reached a conclusion. That

The Oak Ridge Competitive Electricity Dispatch (ORCED) Model Version 9

Energy Technology Data Exchange (ETDEWEB)

Hadley, Stanton W. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Baek, Young Sun [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2016-11-01

The Oak Ridge Competitive Electricity Dispatch (ORCED) model dispatches power plants in a region to meet the electricity demands for any single given year up to 2030. It uses publicly available sources of data describing electric power units such as the National Energy Modeling System and hourly demands from utility submittals to the Federal Energy Regulatory Commission that are projected to a future year. The model simulates a single region of the country for a given year, matching generation to demands and predefined net exports from the region, assuming no transmission constraints within the region. ORCED can calculate a number of key financial and operating parameters for generating units and regional market outputs including average and marginal prices, air emissions, and generation adequacy. By running the model with and without changes such as generation plants, fuel prices, emission costs, plug-in hybrid electric vehicles, distributed generation, or demand response, the marginal impact of these changes can be found.

Model performance analysis and model validation in logistic regression

Directory of Open Access Journals (Sweden)

Rosa Arboretti Giancristofaro

2007-10-01

Full Text Available In this paper a new model validation procedure for a logistic regression model is presented. At first, we illustrate a brief review of different techniques of model validation. Next, we define a number of properties required for a model to be considered "good", and a number of quantitative performance measures. Lastly, we describe a methodology for the assessment of the performance of a given model by using an example taken from a management study.

Model building strategy for logistic regression: purposeful selection.

Science.gov (United States)

Zhang, Zhongheng

2016-03-01

Logistic regression is one of the most commonly used models to account for confounders in medical literature. The article introduces how to perform purposeful selection model building strategy with R. I stress on the use of likelihood ratio test to see whether deleting a variable will have significant impact on model fit. A deleted variable should also be checked for whether it is an important adjustment of remaining covariates. Interaction should be checked to disentangle complex relationship between covariates and their synergistic effect on response variable. Model should be checked for the goodness-of-fit (GOF). In other words, how the fitted model reflects the real data. Hosmer-Lemeshow GOF test is the most widely used for logistic regression model.

The APT model as reduced-rank regression

NARCIS (Netherlands)

Bekker, P.A.; Dobbelstein, P.; Wansbeek, T.J.

Integrating the two steps of an arbitrage pricing theory (APT) model leads to a reduced-rank regression (RRR) model. So the results on RRR can be used to estimate APT models, making estimation very simple. We give a succinct derivation of estimation of RRR, derive the asymptotic variance of RRR

Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

Science.gov (United States)

Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

2016-01-01

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19

Influence diagnostics in meta-regression model.

Science.gov (United States)

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

2017-09-01

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

Logistic Regression Modeling of Diminishing Manufacturing Sources for Integrated Circuits

National Research Council Canada - National Science Library

Gravier, Michael

1999-01-01

.... The research identified logistic regression as a powerful tool for analysis of DMSMS and further developed twenty models attempting to identify the "best" way to model and predict DMSMS using logistic regression...

Geophysical studies of aseismic ridges in northern Indian Ocean-crustal structure and isostatic models

Digital Repository Service at National Institute of Oceanography (India)

Sreejith, K.M.

. The important conclusions derived from this study are as follows. 1. The Comorin Ridge extends for about 500 km in NNW-SSE direction in north central Indian Ocean, close to the southern tip of India and western continental margin of Sri Lanka. The ridge... topography has variable reliefs along its strike and uneven gradients on both sides of the ridge flanks. The southern part of the ridge (between 1.5°N and 3°N) has an elevation of up to 0.5 km from the adjacent seafloor of about 4 km deep; in the central...

A correlated-cluster model and the ridge phenomenon in hadron–hadron collisions

Energy Technology Data Exchange (ETDEWEB)

Sanchis-Lozano, Miguel-Angel, E-mail: Miguel.Angel.Sanchis@ific.uv.es [Instituto de Física Corpuscular (IFIC) and Departamento de Física Teórica, Centro Mixto Universitat de València-CSIC, Dr. Moliner 50, E-46100 Burjassot, Valencia (Spain); Sarkisyan-Grinbaum, Edward, E-mail: sedward@cern.ch [Experimental Physics Department, CERN, 1211 Geneva 23 (Switzerland); Department of Physics, The University of Texas at Arlington, Arlington, TX 76019 (United States)

2017-03-10

A study of the near-side ridge phenomenon in hadron–hadron collisions based on a cluster picture of multiparticle production is presented. The near-side ridge effect is shown to have a natural explanation in this context provided that clusters are produced in a correlated manner in the collision transverse plane.

A correlated-cluster model and the ridge phenomenon in hadron-hadron collisions

CERN Document Server

Sanchis-Lozano, Miguel-Angel

2017-03-10

A study of the near-side ridge phenomenon in hadron-hadron collisions based on a cluster picture of multiparticle production is presented. The near-side ridge effect is shown to have a natural explanation in this context provided that clusters are produced in a correlated manner in the collision transverse plane.

Analysis of dental caries using generalized linear and count regression models

Directory of Open Access Journals (Sweden)

Javali M. Phil

2013-11-01

Full Text Available Generalized linear models (GLM are generalization of linear regression models, which allow fitting regression models to response data in all the sciences especially medical and dental sciences that follow a general exponential family. These are flexible and widely used class of such models that can accommodate response variables. Count data are frequently characterized by overdispersion and excess zeros. Zero-inflated count models provide a parsimonious yet powerful way to model this type of situation. Such models assume that the data are a mixture of two separate data generation processes: one generates only zeros, and the other is either a Poisson or a negative binomial data-generating process. Zero inflated count regression models such as the zero-inflated Poisson (ZIP, zero-inflated negative binomial (ZINB regression models have been used to handle dental caries count data with many zeros. We present an evaluation framework to the suitability of applying the GLM, Poisson, NB, ZIP and ZINB to dental caries data set where the count data may exhibit evidence of many zeros and over-dispersion. Estimation of the model parameters using the method of maximum likelihood is provided. Based on the Vuong test statistic and the goodness of fit measure for dental caries data, the NB and ZINB regression models perform better than other count regression models.

Thickness of Knox Group overburden on Central Chestnut Ridge, Oak Ridge Reservation

International Nuclear Information System (INIS)

Staub, W.P.; Hopkins, R.A.

1984-05-01

The thickness of residual soil overlying the Knox Group along Central Chestnut Ridge was estimated by a conventional seismic refraction survey. The purpose of this survey was to identify sites on the Department of Energy's Oak Ridge Reservation where ample overburden exists above the water table for the shallow land burial of low-level radioactive waste. The results of the survey suggest that the upper slopes of the higher ridges in the area have a minimum of 16 to 26 m (52 to 85 ft) of overburden and that the crests of these ridges may have more than 30 m (100 ft). Therefore, it is unlikely that sound bedrock would be encountered during trench excavation [maximum of 10 m (32 ft)] along Central Chestnut Ridge. Also, the relatively low seismic wave velocities measured in the overburden suggest that the water table is generally deep. On the basis of these preliminary results, Central Chestnut Ridge appears to be suitable for further site characterization for the shallow land burial of low-level radioactive waste. 3 references, 5 figures, 1 table

AIRLINE ACTIVITY FORECASTING BY REGRESSION MODELS

Directory of Open Access Journals (Sweden)

Н. Білак

2012-04-01

Full Text Available Proposed linear and nonlinear regression models, which take into account the equation of trend and seasonality indices for the analysis and restore the volume of passenger traffic over the past period of time and its prediction for future years, as well as the algorithm of formation of these models based on statistical analysis over the years. The desired model is the first step for the synthesis of more complex models, which will enable forecasting of passenger (income level airline with the highest accuracy and time urgency.

Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it

NARCIS (Netherlands)

P.D. Grünwald (Peter); T. van Ommen (Thijs)

2017-01-01

textabstractWe empirically show that Bayesian inference can be inconsistent under misspecification in simple linear regression problems, both in a model averaging/selection and in a Bayesian ridge regression setting. We use the standard linear model, which assumes homoskedasticity, whereas the data

Inconsistency of Bayesian Inference for Misspecified Linear Models, and a Proposal for Repairing It

NARCIS (Netherlands)

Grünwald, P.; van Ommen, T.

2017-01-01

We empirically show that Bayesian inference can be inconsistent under misspecification in simple linear regression problems, both in a model averaging/selection and in a Bayesian ridge regression setting. We use the standard linear model, which assumes homoskedasticity, whereas the data are

[Application of detecting and taking overdispersion into account in Poisson regression model].

Science.gov (United States)

Bouche, G; Lepage, B; Migeot, V; Ingrand, P

2009-08-01

Researchers often use the Poisson regression model to analyze count data. Overdispersion can occur when a Poisson regression model is used, resulting in an underestimation of variance of the regression model parameters. Our objective was to take overdispersion into account and assess its impact with an illustration based on the data of a study investigating the relationship between use of the Internet to seek health information and number of primary care consultations. Three methods, overdispersed Poisson, a robust estimator, and negative binomial regression, were performed to take overdispersion into account in explaining variation in the number (Y) of primary care consultations. We tested overdispersion in the Poisson regression model using the ratio of the sum of Pearson residuals over the number of degrees of freedom (chi(2)/df). We then fitted the three models and compared parameter estimation to the estimations given by Poisson regression model. Variance of the number of primary care consultations (Var[Y]=21.03) was greater than the mean (E[Y]=5.93) and the chi(2)/df ratio was 3.26, which confirmed overdispersion. Standard errors of the parameters varied greatly between the Poisson regression model and the three other regression models. Interpretation of estimates from two variables (using the Internet to seek health information and single parent family) would have changed according to the model retained, with significant levels of 0.06 and 0.002 (Poisson), 0.29 and 0.09 (overdispersed Poisson), 0.29 and 0.13 (use of a robust estimator) and 0.45 and 0.13 (negative binomial) respectively. Different methods exist to solve the problem of underestimating variance in the Poisson regression model when overdispersion is present. The negative binomial regression model seems to be particularly accurate because of its theorical distribution ; in addition this regression is easy to perform with ordinary statistical software packages.

Variable Selection for Regression Models of Percentile Flows

Science.gov (United States)

Fouad, G.

2017-12-01

Percentile flows describe the flow magnitude equaled or exceeded for a given percent of time, and are widely used in water resource management. However, these statistics are normally unavailable since most basins are ungauged. Percentile flows of ungauged basins are often predicted using regression models based on readily observable basin characteristics, such as mean elevation. The number of these independent variables is too large to evaluate all possible models. A subset of models is typically evaluated using automatic procedures, like stepwise regression. This ignores a large variety of methods from the field of feature (variable) selection and physical understanding of percentile flows. A study of 918 basins in the United States was conducted to compare an automatic regression procedure to the following variable selection methods: (1) principal component analysis, (2) correlation analysis, (3) random forests, (4) genetic programming, (5) Bayesian networks, and (6) physical understanding. The automatic regression procedure only performed better than principal component analysis. Poor performance of the regression procedure was due to a commonly used filter for multicollinearity, which rejected the strongest models because they had cross-correlated independent variables. Multicollinearity did not decrease model performance in validation because of a representative set of calibration basins. Variable selection methods based strictly on predictive power (numbers 2-5 from above) performed similarly, likely indicating a limit to the predictive power of the variables. Similar performance was also reached using variables selected based on physical understanding, a finding that substantiates recent calls to emphasize physical understanding in modeling for predictions in ungauged basins. The strongest variables highlighted the importance of geology and land cover, whereas widely used topographic variables were the weakest predictors. Variables suffered from a high

Microgravity survey of the Oak Ridge K-25 Site, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

Kaufmann, R.D.

1996-05-01

Karst features are known to exist within the carbonate bedrock of the Oak Ridge K-25 Site and may play an important role in groundwater flow and contaminant migration. This report discusses the results of a microgravity survey of the Oak Ridge K-25 Site. The main objective of the survey is to identify areas containing bedrock cavities. Secondary objectives included correlating the observed gravity to the geology and to variations in overburden thickness. The analysis includes 11 profile lines that are oriented perpendicular to the geologic strike and major structures throughout the K-25 Site. The profile lines are modeled in an effort to relate gravity anomalies to karst features such as concentrations of mud-filled cavities. Regolith thickness and density data provided by boreholes constrain the models. Areally distributed points are added to the profile lines to produce a gravity contour map of the site. In addition, data from the K-901 area are combined with data from previous surveys to produce a high resolution map of that site. The K-25 Site is located in an area of folded and faulted sedimentary rocks within the Appalachian Valley and Ridge physiographic province. Paleozoic age rocks of the Rome Formation, Knox Group, and Chickamauga Supergroup underlie the K-25 Site and contain structures that include the Whiteoak Mountain Fault, the K-25 Fault, a syncline, and an anticline. The mapped locations of the rock units and complex structures are currently derived from outcrop and well log analysis

[Evaluation of estimation of prevalence ratio using bayesian log-binomial regression model].

Science.gov (United States)

Gao, W L; Lin, H; Liu, X N; Ren, X W; Li, J S; Shen, X P; Zhu, S L

2017-03-10

To evaluate the estimation of prevalence ratio ( PR ) by using bayesian log-binomial regression model and its application, we estimated the PR of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea in their infants by using bayesian log-binomial regression model in Openbugs software. The results showed that caregivers' recognition of infant' s risk signs of diarrhea was associated significantly with a 13% increase of medical care-seeking. Meanwhile, we compared the differences in PR 's point estimation and its interval estimation of medical care-seeking prevalence to caregivers' recognition of risk signs of diarrhea and convergence of three models (model 1: not adjusting for the covariates; model 2: adjusting for duration of caregivers' education, model 3: adjusting for distance between village and township and child month-age based on model 2) between bayesian log-binomial regression model and conventional log-binomial regression model. The results showed that all three bayesian log-binomial regression models were convergence and the estimated PRs were 1.130(95 %CI : 1.005-1.265), 1.128(95 %CI : 1.001-1.264) and 1.132(95 %CI : 1.004-1.267), respectively. Conventional log-binomial regression model 1 and model 2 were convergence and their PRs were 1.130(95 % CI : 1.055-1.206) and 1.126(95 % CI : 1.051-1.203), respectively, but the model 3 was misconvergence, so COPY method was used to estimate PR , which was 1.125 (95 %CI : 1.051-1.200). In addition, the point estimation and interval estimation of PRs from three bayesian log-binomial regression models differed slightly from those of PRs from conventional log-binomial regression model, but they had a good consistency in estimating PR . Therefore, bayesian log-binomial regression model can effectively estimate PR with less misconvergence and have more advantages in application compared with conventional log-binomial regression model.

Explorative methods in linear models

DEFF Research Database (Denmark)

Høskuldsson, Agnar

2004-01-01

The author has developed the H-method of mathematical modeling that builds up the model by parts, where each part is optimized with respect to prediction. Besides providing with better predictions than traditional methods, these methods provide with graphic procedures for analyzing different feat...... features in data. These graphic methods extend the well-known methods and results of Principal Component Analysis to any linear model. Here the graphic procedures are applied to linear regression and Ridge Regression....

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.

Modeling maximum daily temperature using a varying coefficient regression model

Science.gov (United States)

Han Li; Xinwei Deng; Dong-Yum Kim; Eric P. Smith

2014-01-01

Relationships between stream water and air temperatures are often modeled using linear or nonlinear regression methods. Despite a strong relationship between water and air temperatures and a variety of models that are effective for data summarized on a weekly basis, such models did not yield consistently good predictions for summaries such as daily maximum temperature...

Lepini Mountains Carbonatic Ridge: try of springs recharge areas verification and water exchange quantification with Pontina Plain by use of a numerical model (Central Italy

Directory of Open Access Journals (Sweden)

Pamela Teoli

2014-03-01

Full Text Available The study area of this work is represented by the Lepini Mountains carbonatic ridge and by the Pontina Plain foothills area, on which in the past, within quantitative hydrogeological characterizations, models were developed for calculating the groundwater flow, but only referred to the ridge. The most recent studies (Teoli, 2012 have done their best, instead, to represent the underground water exchanges between the ridge and the Pontina Plain foothill area. The new model (developed using computer code MODFLOW 2005 has been implemented to simulate steady-state underground flow using equivalent porous media approach even for the ridge; attention has been particularly directed to the proper tectonic ridge schematic, which the authors had previously defined, together with others (Alimonti et al., 2010, on detailed structural-geological survey basis, integrated by hydrogeological analysis. So, it’s been possible to determine partitioning effects on groundwater flowpaths and on springs recharge areas extent, whose total average discharge is about 10m3/s. Model calibration main goal has been the recharge areas permeability definition, posing the correspondence of calculated flows with measured springs’ flows; as a consequence, it’s been possible to improve the model reliability (uncertainty reduction quantifying the flow residuals’ standard deviation offset.

Observation of pressure ridges in SAR images of sea ice: Scattering theory and comparison with observations

Science.gov (United States)

Vesecky, J. F.; Daida, J. M.; Shuchman, R. A.; Onstott, R. H.; Camiso, J. C.

1993-01-01

Ridges and keels (hummocks and bummocks) in sea ice flows are important in sea ice research for both scientific and practical reasons. Sea ice movement and deformation is driven by internal and external stresses on the ice. Ridges and keels play important roles in both cases because they determine the external wind and current stresses via drag coefficients. For example, the drag coefficient over sea ice can vary by a factor of several depending on the fluid mechanical roughness length of the surface. This roughness length is thought to be strongly dependent on the ridge structures present. Thus, variations in ridge and keel structure can cause gradients in external stresses which must be balanced by internal stresses and possibly fracture of the ice. Ridging in sea ice is also a sign of fracture. In a practical sense, large ridges form the biggest impediment to surface travel over the ice or penetration through sea ice by ice-strengthened ships. Ridges also play an important role in the damage caused by sea ice to off-shore structures. Hence, observation and measurement of sea ice ridges is an important component of sea ice remote sensing. The research reported here builds on previous work, estimating the characteristics of ridges and leads in sea ice from SAR images. Our objective is to develop methods for quantitative measurement of sea ice ridges from SAR images. To make further progress, in particular, to estimate ridge height, a scattering model for ridges is needed. Our research approach for a ridge scattering model begins with a survey of the geometrical properties of ridges and a comparison with the characteristics of the surrounding ice. For this purpose we have used airborne optical laser (AOL) data collected during the 1987 Greenland Sea Experiment. These data were used to generate a spatial wavenumber spectrum for height variance for a typical ridge - the typical ridge is the average over 10 large ridges. Our first-order model radar scattering includes

Measurement of pressure ridges in SAR images of sea ice - Preliminary results on scattering theory

Science.gov (United States)

Vesecky, J. F.; Smith, M. P.; Daida, J. M.; Samadani, R.; Camiso, J. C.

1992-01-01

Sea ice ridges and keels (hummocks and bummocks) are important in sea ice research for both scientific and practical reasons. A long-term objective is to make quantitative measurements of sea ice ridges using synthetic aperture radar (SAR) images. The preliminary results of a scattering model for sea ice ridge are reported. The approach is through the ridge height variance spectrum Psi(K), where K is the spatial wavenumber, and the two-scale scattering model. The height spectrum model is constructed to mimic height statistics observed with an airborne optical laser. The spectrum model is used to drive a two-scale scattering model. Model results for ridges observed at C- and X-band yield normalized radar cross sections that are 10 to 15 dB larger than the observed cross sections of multiyear ice over the range of angles of incidence from 10 to 70 deg.

Prediction of facial height, width, and ratio from thumbprints ridge count and its possible applications

Directory of Open Access Journals (Sweden)

Lawan Hassan Adamu

2017-01-01

Full Text Available The fingerprints and face recognition are two biometric processes that comprise methods for uniquely recognizing humans based on certain number of intrinsic physical or behavioral traits. The objectives of the study were to predict the facial height (FH, facial width, and ratios from thumbprints ridge count and its possible applications. This was a cross-sectional study. A total of 457 participants were recruited. A fingerprint live scanner was used to capture the plain thumbprint. The facial photograph was captured using a digital camera. Pearson's correlation analysis was used for the relationship between thumbprint ridge density and facial linear dimensions. Step-wise linear multiple regression analysis was used to predict facial distances from thumbprint ridge density. The result showed that in males the right ulnar ridge count correlates negatively with lower facial width (LFW, upper facial width/upper FH (UFW/UFH, lower FH/FH (LFH/FH, and positively with UFH and UFW/LFW. The right and left proximal ridge counts correlate with LFW and UFH, respectively. In males, the right ulnar ridge count predicts LFW, UFW/LFW, UFW/UFH, and LFH/FH. Special upper face height I, LFW, height of lower third of the face, UFW/LFW was predicted by right radial ridge counts. LFH, height of lower third of the face, and LFH/FH were predicted from left ulnar ridge count whereas left proximal ridge count predicted LFW. In females only, the special upper face height I was predicted by right ulnar ridge count. In conclusion, thumbprint ridge counts can be used to predict FH, width, ratios among Hausa population. The possible application of fingerprints in facial characterization for used in human biology, paleodemography, and forensic science was demonstrated.

Modeling the morphodynamics of shoreface-connected sand ridges

NARCIS (Netherlands)

Vis-star, N.C.

2008-01-01

The focus of this thesis is on the morphodynamics of shoreface-connected sand ridges, which are large-scale bedforms observed on the inner shelf of coastal seas where storms occur frequently. The main aim was to explore which physical processes control the formation, long-term evolution and main

Grain-Size Dynamics Beneath Mid-Ocean Ridges: Implications for Permeability and Melt Extraction

Science.gov (United States)

Turner, A. J.; Katz, R. F.; Behn, M. D.

2014-12-01

The permeability structure of the sub-ridge mantle plays an important role in how melt is focused and extracted at mid-ocean ridges. Permeability is controlled by porosity and the grain size of the solid mantle matrix, which is in turn controlled by the deformation conditions. To date, models of grain size evolution and mantle deformation have not been coupled to determine the influence of spatial variations in grain-size on the permeability structure at mid-ocean ridges. Rather, current models typically assume a constant grain size for the whole domain [1]. Here, we use 2-D numerical models to evaluate the influence of grain-size variability on the permeability structure beneath a mid-ocean ridge and use these results to speculate on the consequences for melt focusing and extraction. We construct a two-dimensional, single phase model for the steady-state grain size beneath a mid-ocean ridge. The model employs a composite rheology of diffusion creep, dislocation creep, dislocation accommodated grain boundary sliding, and a brittle stress limiter. Grain size is calculated using the "wattmeter" model of Austin and Evans [2]. We investigate the sensitivity of the model to global variations in grain growth exponent, potential temperature, spreading-rate, and grain boundary sliding parameters [3,4]. Our model predicts that permeability varies by two orders of magnitude due to the spatial variability of grain size within the expected melt region of a mid-ocean ridge. The predicted permeability structure suggests grain size may promote focusing of melt towards the ridge axis. Furthermore, the calculated grain size structure should focus melt from a greater depth than models that exclude grain-size variability. Future work will involve evaluating this hypothesis by implementing grain-size dynamics within a two-phase mid-ocean ridge model. The developments of such a model will be discussed. References: [1] R. F. Katz, Journal of Petrology, volume 49, issue 12, page 2099

Posterior Mandibular Ridge Resorption Associated with Different Retentive Systems for Overdentures: A 7-Year Retrospective Preliminary Study.

Science.gov (United States)

Elsyad, Moustafa Abdou; Mohamed, Shahinaz Sayed; Shawky, Ahmad Fathalla

This retrospective study compared posterior mandibular residual ridge resorption with two different retentive mechanisms for overdentures after 7 years. A convenience sample of 18 edentulous men was assigned to one of two equal groups. Two implants were placed in the mandibular canine areas for each patient using the conventional two-stage surgical protocol, and the implants were splinted with a round bar 3 months later. New mandibular overdentures were then connected to the bars with clips (clip-retained overdentures, CR group) or resilient liners (resilient liner-retained overdentures, RR group). Posterior mandibular ridge resorption (PMRR) was recorded using proportional measurements and posterior area index (PAI) on panoramic radiographs taken immediately after overdenture insertion (T 0 ) and 7 years later (T 7 ). A linear regression model was used to verify the relationship between PAI and the following considerations: attachment type, age, initial mandibular ridge height, period of mandibular edentulism, number of previously worn dentures, and relining events. After 7 years, the RR group demonstrated a significantly (P = .014) higher change in PAI (0.11 ± 0.02) than the CR group (0.06 ± 0.04). The average PMRR for each mm of posterior mandibular ridge was 0.79 mm (0.11 mm/year) in the CR group and 1.4 mm (0.2 mm/year) in the RR group. Attachment type, initial mandibular ridge height, and relining times were significantly correlated with change in the PAI (P = .004, P = .035, and P = .045, respectively). Within the limitations of this preliminary study's design, it was observed that following a 7-year period of use, resilient liner attachments for bar/implant-retained overdentures appear to be associated with greater posterior mandibular ridge resorption when compared to clip attachments.

Structured Additive Regression Models: An R Interface to BayesX

Directory of Open Access Journals (Sweden)

Nikolaus Umlauf

2015-02-01

Full Text Available Structured additive regression (STAR models provide a flexible framework for model- ing possible nonlinear effects of covariates: They contain the well established frameworks of generalized linear models and generalized additive models as special cases but also allow a wider class of effects, e.g., for geographical or spatio-temporal data, allowing for specification of complex and realistic models. BayesX is standalone software package providing software for fitting general class of STAR models. Based on a comprehensive open-source regression toolbox written in C++, BayesX uses Bayesian inference for estimating STAR models based on Markov chain Monte Carlo simulation techniques, a mixed model representation of STAR models, or stepwise regression techniques combining penalized least squares estimation with model selection. BayesX not only covers models for responses from univariate exponential families, but also models from less-standard regression situations such as models for multi-categorical responses with either ordered or unordered categories, continuous time survival data, or continuous time multi-state models. This paper presents a new fully interactive R interface to BayesX: the R package R2BayesX. With the new package, STAR models can be conveniently specified using Rs formula language (with some extended terms, fitted using the BayesX binary, represented in R with objects of suitable classes, and finally printed/summarized/plotted. This makes BayesX much more accessible to users familiar with R and adds extensive graphics capabilities for visualizing fitted STAR models. Furthermore, R2BayesX complements the already impressive capabilities for semiparametric regression in R by a comprehensive toolbox comprising in particular more complex response types and alternative inferential procedures such as simulation-based Bayesian inference.

Multiple regression and beyond an introduction to multiple regression and structural equation modeling

CERN Document Server

Keith, Timothy Z

2014-01-01

Multiple Regression and Beyond offers a conceptually oriented introduction to multiple regression (MR) analysis and structural equation modeling (SEM), along with analyses that flow naturally from those methods. By focusing on the concepts and purposes of MR and related methods, rather than the derivation and calculation of formulae, this book introduces material to students more clearly, and in a less threatening way. In addition to illuminating content necessary for coursework, the accessibility of this approach means students are more likely to be able to conduct research using MR or SEM--and more likely to use the methods wisely. Covers both MR and SEM, while explaining their relevance to one another Also includes path analysis, confirmatory factor analysis, and latent growth modeling Figures and tables throughout provide examples and illustrate key concepts and techniques For additional resources, please visit: http://tzkeith.com/.

Time series regression model for infectious disease and weather.

Science.gov (United States)

Imai, Chisato; Armstrong, Ben; Chalabi, Zaid; Mangtani, Punam; Hashizume, Masahiro

2015-10-01

Time series regression has been developed and long used to evaluate the short-term associations of air pollution and weather with mortality or morbidity of non-infectious diseases. The application of the regression approaches from this tradition to infectious diseases, however, is less well explored and raises some new issues. We discuss and present potential solutions for five issues often arising in such analyses: changes in immune population, strong autocorrelations, a wide range of plausible lag structures and association patterns, seasonality adjustments, and large overdispersion. The potential approaches are illustrated with datasets of cholera cases and rainfall from Bangladesh and influenza and temperature in Tokyo. Though this article focuses on the application of the traditional time series regression to infectious diseases and weather factors, we also briefly introduce alternative approaches, including mathematical modeling, wavelet analysis, and autoregressive integrated moving average (ARIMA) models. Modifications proposed to standard time series regression practice include using sums of past cases as proxies for the immune population, and using the logarithm of lagged disease counts to control autocorrelation due to true contagion, both of which are motivated from "susceptible-infectious-recovered" (SIR) models. The complexity of lag structures and association patterns can often be informed by biological mechanisms and explored by using distributed lag non-linear models. For overdispersed models, alternative distribution models such as quasi-Poisson and negative binomial should be considered. Time series regression can be used to investigate dependence of infectious diseases on weather, but may need modifying to allow for features specific to this context. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Linear regression crash prediction models : issues and proposed solutions.

Science.gov (United States)

2010-05-01

The paper develops a linear regression model approach that can be applied to : crash data to predict vehicle crashes. The proposed approach involves novice data aggregation : to satisfy linear regression assumptions; namely error structure normality ...

Identification of Influential Points in a Linear Regression Model

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Jan Grosz

2011-03-01

Full Text Available The article deals with the detection and identification of influential points in the linear regression model. Three methods of detection of outliers and leverage points are described. These procedures can also be used for one-sample (independentdatasets. This paper briefly describes theoretical aspects of several robust methods as well. Robust statistics is a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. A simulation model of the simple linear regression is presented.

Testing Predictions of a Landscape Evolution Model Using the Dragon’s Back Pressure Ridge as a Natural Experiment

Science.gov (United States)

Perignon, M. C.; Tucker, G. E.; Hilley, G. E.; Arrowsmith, R.

2009-12-01

Landscape evolution models use mass transport rules to simulate the temporal development of topography over timescales too long for humans to observe. As such, these models are difficult to test using the decadal time-scale observations of topographic change that can be directly measured. In contrast, natural systems in which driving forces, boundary conditions, and timing of landscape evolution over millennial time-scales can be well constrained may be used to test the ability of mathematical models to reproduce various attributes of the observed topography. The Dragon’s Back pressure ridge, a 4km x 0.5 km x 100 m high area of elevated topography elongate parallel to the south-central San Andreas fault (SAF) in California, serves as a natural laboratory for studying how the timing and spatial distribution of uplift affects patterns of erosion and topography. Geologic mapping and geophysical studies show that, at this location, the Pacific plate is forced over a relatively stationary shallow discontinuity in the SAF, resulting in local uplift. Continued right-lateral motion along the fault results in the movement of material though the uplift zone at the SAF slip rate of 35 mm/yr. This allows for the substitution of space for time when observing topographic change, and can be used to constrain the tectonic conditions to which the surface processes responded and developed the resulting landscape. We used the CHILD model of landscape evolution to recreate the Dragon’s Back pressure ridge system in order to test the reliability of the model predictions and determine the necessary and sufficient conditions to explain the observed topography. To do this, we first ran a Monte Carlo simulation in which we varied the model inputs within a range of plausible values. We then compared the model results with LiDAR topography from the Dragon’s Back pressure ridge to determine which combinations of input parameters best reproduced the observed topography and how well it

Unfaulting the Sardarapat Ridge, Southwest Armenia

Science.gov (United States)

Wetmore, P.; Connor, C.; Connor, L. J.; Savov, I. P.; Karakhanyan, A.

2012-12-01

Armenia is located near the core of contractional deformation associated with the collision between the Arabian and Eurasian tectonic plates. Several studies of this region, including portions of adjacent Georgia, Iran, and Turkey, have indicated that 1-2 mm/yr of intra-plate, north-south shortening is primarily accommodated by a network of E-W trending thrust faults, and NW-trending (dextral) and NE-trending (sinistral) strike-slip faults. One proposed fault in this network, the Sardarapat Fault (SF), was investigated as part of a regional seismic hazard assessment ahead of the installation of a replacement reactor at the Armenian Nuclear Power Plant (ANPP). The SF is primarily defined by the Sardarapat Ridge (SR), which is a WNW-trending, 40-70 m high topographic feature located just north of the Arax River and the Turkey-Armenia border. The stratigraphy comprising this ridge includes alluvium overlying several meters of lacustrine deposits above a crystal-rich basaltic lava flow that yields an Ar-Ar age of 0.9 +/- 0.02 Ma. The alluvial sediments on the ridge contain early Bronze age (3832-3470 BP) artifacts at an elevation 25 m above those of the surrounding alluvial plane. This has lead to the suggestion that the SR is bound to the south (the steepest side) by the SF, which is uplifting the ridge at a rate of 0.7 mm/yr. However, despite the prominence and trend of the ridge there are no unequivocal observations, such as scarps or exposures of fault rocks, to support the existence of the SF. The goal of the investigation of the SR area was to test various models for the formation of the ridge including faulting and combined volcanic and erosional processes. We therefore collected gravimetric, magnetic, magneto-tellurics (MT), and transient electromagnetic (TEM) data across an area of ~400 km2, and used correlations of stratigraphic data from coreholes drilled proximal to the study area to define the geometry of the contact between the basement and basin fill to

The art of regression modeling in road safety

CERN Document Server

Hauer, Ezra

2015-01-01

This unique book explains how to fashion useful regression models from commonly available data to erect models essential for evidence-based road safety management and research. Composed from techniques and best practices presented over many years of lectures and workshops, The Art of Regression Modeling in Road Safety illustrates that fruitful modeling cannot be done without substantive knowledge about the modeled phenomenon. Class-tested in courses and workshops across North America, the book is ideal for professionals, researchers, university professors, and graduate students with an interest in, or responsibilities related to, road safety. This book also: · Presents for the first time a powerful analytical tool for road safety researchers and practitioners · Includes problems and solutions in each chapter as well as data and spreadsheets for running models and PowerPoint presentation slides · Features pedagogy well-suited for graduate courses and workshops including problems, solutions, and PowerPoint p...

Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

Directory of Open Access Journals (Sweden)

Hong-Juan Li

2013-04-01

Full Text Available Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR, this paper presents a SVR model hybridized with the empirical mode decomposition (EMD method and auto regression (AR for electric load forecasting. The electric load data of the New South Wales (Australia market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

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

Science.gov (United States)

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

2018-05-01

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

Flexible competing risks regression modeling and goodness-of-fit

DEFF Research Database (Denmark)

Scheike, Thomas; Zhang, Mei-Jie

2008-01-01

In this paper we consider different approaches for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause...... models that is easy to fit and contains the Fine-Gray model as a special case. One advantage of this approach is that our regression modeling allows for non-proportional hazards. This leads to a new simple goodness-of-fit procedure for the proportional subdistribution hazards assumption that is very easy...... of the flexible regression models to analyze competing risks data when non-proportionality is present in the data....

Topography and vegetation as predictors of snow water equivalent across the alpine treeline ecotone at Lee Ridge, Glacier National Park, Montana, U.S.A.

Science.gov (United States)

Geddes, C.A.; Brown, D.G.; Fagre, D.B.

2005-01-01

We derived and implemented two spatial models of May snow water equivalent (SWE) at Lee Ridge in Glacier National Park, Montana. We used the models to test the hypothesis that vegetation structure is a control on snow redistribution at the alpine treeline ecotone (ATE). The statistical models were derived using stepwise and "best" subsets regression techniques. The first model was derived from field measurements of SWE, topography, and vegetation taken at 27 sample points. The second model was derived using GIS-based measures of topography and vegetation. Both the field- (R² = 0.93) and GIS-based models (R² = 0.69) of May SWE included the following variables: site type (based on vegetation), elevation, maximum slope, and general slope aspect. Site type was identified as the most important predictor of SWE in both models, accounting for 74.0% and 29.5% of the variation, respectively. The GIS-based model was applied to create a predictive map of SWE across Lee Ridge, predicting little snow accumulation on the top of the ridge where vegetation is scarce. The GIS model failed in large depressions, including ephemeral stream channels. The models supported the hypothesis that upright vegetation has a positive effect on accumulation of SWE above and beyond the effects of topography. Vegetation, therefore, creates a positive feedback in which it modifies its, environment and could affect the ability of additional vegetation to become established.

Ridge Orientations of the Ridge-Forming Unit, Sinus Meridiani, Mars-A Fluvial Explanation

Science.gov (United States)

Wilkinson, M. Justin; Herridge, A.

2013-01-01

Imagery and MOLA data were used in an analysis of the ridge-forming rock unit (RFU) exposed in Sinus Meridiani (SM). This unit shows parallels at different scales with fluvial sedimentary bodies. We propose the terrestrial megafan as the prime analog for the RFU, and likely for other members of the layered units. Megafans are partial cones of fluvial sediment, with radii up to hundreds of km. Although recent reviews of hypotheses for the RFU units exclude fluvial hypotheses [1], inverted ridges in the deserts of Oman have been suggested as putative analogs for some ridges [2], apparently without appreciating The wider context in which these ridges have formed is a series of megafans [3], a relatively unappreciated geomorphic feature. It has been argued that these units conform to the megafan model at the regional, subregional and local scales [4]. At the regional scale suites of terrestrial megafans are known to cover large areas at the foot of uplands on all continents - a close parallel with the setting of the Meridiani sediments at the foot of the southern uplands of Mars, with its incised fluvial systems leading down the regional NW slope [2, 3] towards the sedimentary units. At the subregional scale the layering and internal discontinuities of the Meridiani rocks are consistent, inter alia, with stacked fluvial units [4]. Although poorly recognized as such, the prime geomorphic environment in which stream channel networks cover large areas, without intervening hillslopes, is the megafan [see e.g. 4]. Single megafans can reach 200,000 km2 [5]. Megafans thus supply an analog for areas where channel-like ridges (as a palimpsest of a prior landscape) cover the intercrater plains of Meridiani [6]. At the local, or river-reach scale, the numerous sinuous features of the RFU are suggestive of fluvial channels. Cross-cutting relationships, a common feature of channels on terrestrial megafans, are ubiquitous. Desert megafans show cemented paleo-channels as inverted

Bioinspired design of a ridging shovel with anti-adhesive and drag reducing

Directory of Open Access Journals (Sweden)

Zhijun Zhang

2015-03-01

Full Text Available Learning from the microstructure of the convex (concave and ridging (triangle and arc-shaped shapes of fresh lotus leaves and shark skin, bionic ridging shovels was designed with the characteristics of adhesion and resistance reduction. Ten ridging shovel models were established, and the interaction process with the soil by ANSYS is discussed. Stress analysis results showed that the bionic ridging shovel was more obvious in visbreaking and in the resistance reduction effect. An indoor soil bin experiment with the bionic ridging shovel and the prototype ridging shovel was operated as follows: the ridging resistance of the three types of ridging shovel was tested under the condition of two soil moistures (18.61% and 20.9% and three different ridging speeds (0.68, 0.87, and 1.11 m/s. In this article, the structure, the mechanism, and their relationship to the functions are discussed. The results of this study will be useful in practical application in the field of agricultural machinery toward practical use and industrialization.

Hydrodynamic role of longitudinal ridges in a leatherback turtle swimming

Science.gov (United States)

Bang, Kyeongtae; Kim, Jooha; Lee, Sang-Im; Choi, Haecheon

2015-11-01

The leatherback sea turtle (Dermochelys coriacea), the fastest swimmer and the deepest diver among marine turtles, has five longitudinal ridges on its carapace. These ridges are the most remarkable morphological features distinguished from other marine turtles. To investigate the hydrodynamic role of these ridges in the leatherback turtle swimming, we model a carapace with and without ridges by using three dimensional surface data of a stuffed leatherback turtle in the National Science Museum, Korea. The experiment is conducted in a wind tunnel in the ranges of the real leatherback turtle's Reynolds number (Re) and angle of attack (α). The longitudinal ridges function differently according to the flow condition (i.e. Re and α). At low Re and negative α that represent the swimming condition of hatchlings and juveniles, the ridges significantly decrease the drag by generating streamwise vortices and delaying the main separation. On the other hand, at high Re and positive α that represent the swimming condition of adults, the ridges suppress the laminar separation bubble near the front part by generating streamwise vortices and enhance the lift and lift-to-drag ratio. Supported by the NRF program (2011-0028032).

Maximum Entropy Discrimination Poisson Regression for Software Reliability Modeling.

Science.gov (United States)

Chatzis, Sotirios P; Andreou, Andreas S

2015-11-01

Reliably predicting software defects is one of the most significant tasks in software engineering. Two of the major components of modern software reliability modeling approaches are: 1) extraction of salient features for software system representation, based on appropriately designed software metrics and 2) development of intricate regression models for count data, to allow effective software reliability data modeling and prediction. Surprisingly, research in the latter frontier of count data regression modeling has been rather limited. More specifically, a lack of simple and efficient algorithms for posterior computation has made the Bayesian approaches appear unattractive, and thus underdeveloped in the context of software reliability modeling. In this paper, we try to address these issues by introducing a novel Bayesian regression model for count data, based on the concept of max-margin data modeling, effected in the context of a fully Bayesian model treatment with simple and efficient posterior distribution updates. Our novel approach yields a more discriminative learning technique, making more effective use of our training data during model inference. In addition, it allows of better handling uncertainty in the modeled data, which can be a significant problem when the training data are limited. We derive elegant inference algorithms for our model under the mean-field paradigm and exhibit its effectiveness using the publicly available benchmark data sets.

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

Science.gov (United States)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

Bayesian Inference of a Multivariate Regression Model

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Marick S. Sinay

2014-01-01

Full Text Available We explore Bayesian inference of a multivariate linear regression model with use of a flexible prior for the covariance structure. The commonly adopted Bayesian setup involves the conjugate prior, multivariate normal distribution for the regression coefficients and inverse Wishart specification for the covariance matrix. Here we depart from this approach and propose a novel Bayesian estimator for the covariance. A multivariate normal prior for the unique elements of the matrix logarithm of the covariance matrix is considered. Such structure allows for a richer class of prior distributions for the covariance, with respect to strength of beliefs in prior location hyperparameters, as well as the added ability, to model potential correlation amongst the covariance structure. The posterior moments of all relevant parameters of interest are calculated based upon numerical results via a Markov chain Monte Carlo procedure. The Metropolis-Hastings-within-Gibbs algorithm is invoked to account for the construction of a proposal density that closely matches the shape of the target posterior distribution. As an application of the proposed technique, we investigate a multiple regression based upon the 1980 High School and Beyond Survey.

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.

A test for the parameters of multiple linear regression models ...

African Journals Online (AJOL)

A test for the parameters of multiple linear regression models is developed for conducting tests simultaneously on all the parameters of multiple linear regression models. The test is robust relative to the assumptions of homogeneity of variances and absence of serial correlation of the classical F-test. Under certain null and ...

Exploring Microbial Processes with Thermal-Hydrological Models of the Eastern Flank of the Juan de Fuca Ridge

Science.gov (United States)

Weathers, T. S.; Fisher, A. T.; Winslow, D. M.; Stauffer, P. H.; Gable, C. W.

2017-12-01

The flanks of mid-ocean ridges experience coupled flows of fluid, heat, and solutes that are critical for a wide range of global processes, including the cycling of carbon and nutrients, which supports a vast crustal biosphere. Only a few ridge-flank sites have been studied in detail; hydrogeologic conditions and processes in the volcanic crust are best understood on the eastern flank of the Juan de Fuca Ridge. This area has been extensively explored with decades of drilling, submersible, observatory, and survey expeditions and experiments, including the first hole-to-hole tracer injection experiment in the ocean crust. This study describes the development of reactive transport simulations for this ridge-flank setting using three-dimensional coupled (thermal-hydrological) models of crustal-scale circulation, beginning with the exploration of tracer transport. The prevailing flow direction is roughly south to north as a result of outcrop-to-outcrop flow, with a bulk flow rate in the range of meters/year. However, tracer was detected 500 m south ("upstream") from the injection borehole during the first year following injection. This may be explained by local mixing and/or formation fluid discharge from the southern borehole during and after injection. The constraints and parameters required to fit the observed tracer behavior can be used as a basis for modeling reactive transport processes such as nutrient delivery or microbial community evolution as a function of fluid flow. For example, the sulfate concentration in fluid samples from Baby Bare outcrop ( 8 km south of the tracer transport experiment) was 17.8 mmol/kg, whereas at Mama Bare outcrop ( 8 km to north of the tracer transport experiment) the sulfate concentration was 16.3 mmol/mg. By integrating laboratory-derived sulfate reduction rates from microbial samples originating from Juan de Fuca borehole observatories into reactive transport models, we can explore the range of microbial activity that supports

Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

Science.gov (United States)

Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

2016-03-01

In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

A History of Regression and Related Model-Fitting in the Earth Sciences (1636?-2000)

International Nuclear Information System (INIS)

Howarth, Richard J.

2001-01-01

roots in meeting the evident need for improved estimators in spatial interpolation. Technical advances in regression analysis during the 1970s embraced the development of regression diagnostics and consequent attention to outliers; the recognition of problems caused by correlated predictors, and the subsequent introduction of ridge regression to overcome them; and techniques for fitting errors-in-variables and mixture models. Improvements in computational power have enabled ever more computer-intensive methods to be applied. These include algorithms which are robust in the presence of outliers, for example Rousseeuw's 1984 Least Median Squares; nonparametric smoothing methods, such as kernel-functions, splines and Cleveland's 1979 LOcally WEighted Scatterplot Smoother (LOWESS); and the Classification and Regression Tree (CART) technique of Breiman and others in 1984. Despite a continuing improvement in the rate of technology-transfer from the statistical to the earth-science community, despite an abrupt drop to a time-lag of about 10 years following the introduction of digital computers, these more recent developments are only just beginning to penetrate beyond the research community of earth scientists. Examples of applications to problem-solving in the earth sciences are given

Logistic regression models

CERN Document Server

Hilbe, Joseph M

2009-01-01

This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...

Linking Simple Economic Theory Models and the Cointegrated Vector AutoRegressive Model

DEFF Research Database (Denmark)

Møller, Niels Framroze

This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its stru....... Further fundamental extensions and advances to more sophisticated theory models, such as those related to dynamics and expectations (in the structural relations) are left for future papers......This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its......, it is demonstrated how other controversial hypotheses such as Rational Expectations can be formulated directly as restrictions on the CVAR-parameters. A simple example of a "Neoclassical synthetic" AS-AD model is also formulated. Finally, the partial- general equilibrium distinction is related to the CVAR as well...

Multiple Response Regression for Gaussian Mixture Models with Known Labels.

Science.gov (United States)

Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng

2012-12-01

Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.

Extending the linear model with R generalized linear, mixed effects and nonparametric regression models

CERN Document Server

Faraway, Julian J

2005-01-01

Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway''s critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author''s treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. All of the ...

Bayesian approach to errors-in-variables in regression models

Science.gov (United States)

Rozliman, Nur Aainaa; Ibrahim, Adriana Irawati Nur; Yunus, Rossita Mohammad

2017-05-01

In many applications and experiments, data sets are often contaminated with error or mismeasured covariates. When at least one of the covariates in a model is measured with error, Errors-in-Variables (EIV) model can be used. Measurement error, when not corrected, would cause misleading statistical inferences and analysis. Therefore, our goal is to examine the relationship of the outcome variable and the unobserved exposure variable given the observed mismeasured surrogate by applying the Bayesian formulation to the EIV model. We shall extend the flexible parametric method proposed by Hossain and Gustafson (2009) to another nonlinear regression model which is the Poisson regression model. We shall then illustrate the application of this approach via a simulation study using Markov chain Monte Carlo sampling methods.

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

Science.gov (United States)

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

2017-04-08

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

Direction of Effects in Multiple Linear Regression Models.

Science.gov (United States)

Wiedermann, Wolfgang; von Eye, Alexander

2015-01-01

Previous studies analyzed asymmetric properties of the Pearson correlation coefficient using higher than second order moments. These asymmetric properties can be used to determine the direction of dependence in a linear regression setting (i.e., establish which of two variables is more likely to be on the outcome side) within the framework of cross-sectional observational data. Extant approaches are restricted to the bivariate regression case. The present contribution extends the direction of dependence methodology to a multiple linear regression setting by analyzing distributional properties of residuals of competing multiple regression models. It is shown that, under certain conditions, the third central moments of estimated regression residuals can be used to decide upon direction of effects. In addition, three different approaches for statistical inference are discussed: a combined D'Agostino normality test, a skewness difference test, and a bootstrap difference test. Type I error and power of the procedures are assessed using Monte Carlo simulations, and an empirical example is provided for illustrative purposes. In the discussion, issues concerning the quality of psychological data, possible extensions of the proposed methods to the fourth central moment of regression residuals, and potential applications are addressed.

Waste management systems model for energy systems sites on the Oak Ridge Reservation

International Nuclear Information System (INIS)

Rodgers, B.R.; Nehls, J.W. Jr.; Rivera, A.L.; Pechin, W.H.; Genung, R.K.

1986-01-01

There is a model on the Oak Ridge Reservation which provides requirements for determining capacities and capabilities related to low-level, hazardous, and mixed wastes. In FY 1987, the model will be sufficiently advanced to provide various waste management scenarios. These scenarios will be compared technically, operationally, and financially by use of waste characterization data and process simulators that are currently under development. The results of the process simulations will be used to help identify waste treatment, storage, and disposal technologies that need to be demonstrated prior to full-scale development for DOE use. The information derived from this effort will be made available to all DOE facilities

The Effects of Ridge Axis Width on Mantle Melting at Mid-Ocean Ridges

Science.gov (United States)

Montesi, L.; Magni, V.; Gaina, C.

2017-12-01

Mantle upwelling in response to plate divergence produces melt at mid-ocean ridges. Melt starts when the solidus is crossed and stops when conductive cooling overcomes heat advection associated with the upwelling. Most mid-ocean ridge models assume that divergence takes place only in a narrow zone that defines the ridge axis, resulting in a single upwelling. However, more complex patterns of divergence are occasionally observed. The rift axis can be 20 km wide at ultraslow spreading center. Overlapping spreading center contain two parallel axes. Rifting in backarc basins is sometimes organized as a series of parallel spreading centers. Distributing plate divergence over several rifts reduces the intensity of upwelling and limits melting. Can this have a significant effect on the expected crustal thickness and on the mode of melt delivery at the seafloor? We address this question by modeling mantle flow and melting underneath two spreading centers separated by a rigid block. We adopt a non-linear rheology that includes dislocation creep, diffusion creep and yielding and include hydrothermal cooling by enhancing thermal conductivity where yielding takes place. The crustal thickness decreases if the rifts are separated by 30 km or more but only if the half spreading rate is between 1 and 2 cm/yr. At melting depth, a single upwelling remains the norm until the separation of the rifts exceeds a critical value ranging from 15 km in the fastest ridges to more than 50 km at ultraslow spreading centers. The stability of the central upwelling is due to hydrothermal cooling, which prevents hot mantle from reaching the surface at each spreading center. When hydrothermal cooling is suppressed, or the spreading centers are sufficiently separated, the rigid block becomes extremely cold and separates two distinct, highly asymmetric upwellings that may focus melt beyond the spreading center. In that case, melt delivery might drive further and further the divergence centers, whereas

Large fault fabric of the Ninetyeast Ridge implies near-spreading ridge formation

Digital Repository Service at National Institute of Oceanography (India)

Sager, W.W.; Paul, C.F.; Krishna, K.S.; Pringle, M.S.; Eisin, A.E.; Frey, F.A.; Rao, D.G.; Levchenko, O.V.

of the high ridge. At 26°S, prominent NE-SW 97 oriented lineations extend southwest from the ridge. Some appear to connect with N-S fracture 98 zone troughs east of NER, implying that the NE-SW features are fracture zone scars formed after 99 the change... to the 105 ridge (Fig. 3). This is especially true for NER south of ~4°S. Where KNOX06RR crossed a 106 gravity lineation, negative gradient features correspond to troughs whereas positive gradient 107 features result from igneous basement highs (Fig. 3...

A Monte Carlo simulation study comparing linear regression, beta regression, variable-dispersion beta regression and fractional logit regression at recovering average difference measures in a two sample design.

Science.gov (United States)

Meaney, Christopher; Moineddin, Rahim

2014-01-24

In biomedical research, response variables are often encountered which have bounded support on the open unit interval--(0,1). Traditionally, researchers have attempted to estimate covariate effects on these types of response data using linear regression. Alternative modelling strategies may include: beta regression, variable-dispersion beta regression, and fractional logit regression models. This study employs a Monte Carlo simulation design to compare the statistical properties of the linear regression model to that of the more novel beta regression, variable-dispersion beta regression, and fractional logit regression models. In the Monte Carlo experiment we assume a simple two sample design. We assume observations are realizations of independent draws from their respective probability models. The randomly simulated draws from the various probability models are chosen to emulate average proportion/percentage/rate differences of pre-specified magnitudes. Following simulation of the experimental data we estimate average proportion/percentage/rate differences. We compare the estimators in terms of bias, variance, type-1 error and power. Estimates of Monte Carlo error associated with these quantities are provided. If response data are beta distributed with constant dispersion parameters across the two samples, then all models are unbiased and have reasonable type-1 error rates and power profiles. If the response data in the two samples have different dispersion parameters, then the simple beta regression model is biased. When the sample size is small (N0 = N1 = 25) linear regression has superior type-1 error rates compared to the other models. Small sample type-1 error rates can be improved in beta regression models using bias correction/reduction methods. In the power experiments, variable-dispersion beta regression and fractional logit regression models have slightly elevated power compared to linear regression models. Similar results were observed if the

Tutorial on Using Regression Models with Count Outcomes Using R

Directory of Open Access Journals (Sweden)

A. Alexander Beaujean

2016-02-01

Full Text Available Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares either with or without transforming the count variables. In either case, using typical regression for count data can produce parameter estimates that are biased, thus diminishing any inferences made from such data. As count-variable regression models are seldom taught in training programs, we present a tutorial to help educational researchers use such methods in their own research. We demonstrate analyzing and interpreting count data using Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression models. The count regression methods are introduced through an example using the number of times students skipped class. The data for this example are freely available and the R syntax used run the example analyses are included in the Appendix.

Remedial investigation report on Waste Area Group 5 at Oak Ridge National Laboratory, Oak Ridge, Tennessee. Volume 1: Technical summary

International Nuclear Information System (INIS)

1995-03-01

A remedial investigation (RI) was performed to support environmental restoration activities for Waste Area Grouping (WAG) 5 at the Oak Ridge National Laboratory (ORNL) in Oak Ridge, Tennessee. The WAG 5 RI made use of the observational approach, which concentrates on collecting only information needed to assess site risks and support future cleanup work. This information was interpreted and is presented using the framework of the site conceptual model, which relates contaminant sources and release mechanisms to migration pathways and exposure points that are keyed to current and future environmental risks for both human and ecological receptors. The site conceptual model forms the basis of the WAG 5 remedial action strategy and remedial action objectives. The RI provided the data necessary to verify this model and allows recommendations to be made to accomplish those objectives

Remedial investigation report on Waste Area Grouping 5 at Oak Ridge National Laboratory, Oak Ridge, Tennessee. Volume 1: Technical summary

International Nuclear Information System (INIS)

1995-09-01

A remedial investigation (RI) was performed to support environmental restoration activities for Waste Area Grouping (WAG) 5 at the Oak Ridge National Laboratory (ORNL) in Oak Ridge, Tennessee. The WAG 5 RI made use of the observational approach, which concentrates on collecting only information needed to assess site risks and support future cleanup work. This information was interpreted and is presented using the framework of the site conceptual model, which relates contaminant sources and release mechanisms to migration pathways and exposure points that are keyed to current and future environmental risks for both human and ecological receptors. The site conceptual model forms the basis of the WAG 5 remedial action strategy and remedial action objectives. The RI provided the data necessary to verify this model and allows recommendations to be made to accomplish those objectives.

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

Directory of Open Access Journals (Sweden)

Natalija Nakov

2014-06-01

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

Linear regression models for quantitative assessment of left ...

African Journals Online (AJOL)

Changes in left ventricular structures and function have been reported in cardiomyopathies. No prediction models have been established in this environment. This study established regression models for prediction of left ventricular structures in normal subjects. A sample of normal subjects was drawn from a large urban ...

Reduced Rank Regression

DEFF Research Database (Denmark)

Johansen, Søren

2008-01-01

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

Poisson regression for modeling count and frequency outcomes in trauma research.

Science.gov (United States)

Gagnon, David R; Doron-LaMarca, Susan; Bell, Margret; O'Farrell, Timothy J; Taft, Casey T

2008-10-01

The authors describe how the Poisson regression method for analyzing count or frequency outcome variables can be applied in trauma studies. The outcome of interest in trauma research may represent a count of the number of incidents of behavior occurring in a given time interval, such as acts of physical aggression or substance abuse. Traditional linear regression approaches assume a normally distributed outcome variable with equal variances over the range of predictor variables, and may not be optimal for modeling count outcomes. An application of Poisson regression is presented using data from a study of intimate partner aggression among male patients in an alcohol treatment program and their female partners. Results of Poisson regression and linear regression models are compared.

Sedimentary characteristics and controlling factors of shelf sand ridges in the Pearl River Mouth Basin, northeast of South China Sea

Directory of Open Access Journals (Sweden)

Xiangtao Zhang

2017-04-01

Full Text Available Shelf sand ridge is a significant type of reservoir in the continental marginal basin, and it has drawn so much attention from sedimentologists and petroleum geologists. We were able to investigate the morphology, distribution, and sedimentary structures of shelf sand ridges systematically in this study based on the integration of high-resolution 3D seismic data, well logging, and cores. These shelf sand ridges are an asymmetrical mound-like structure in profiles, and they developed on an ancient uplift in the forced regression system tract and are onlapped by the overlying strata. In the plane, shelf sand ridges present as linear-shaped, which is different from the classical radial pattern; not to mention, they are separated into two parts by low amplitude tidal muddy channels. Corrugated bedding, tidal bedding, and scouring features are distinguished in cores of shelf sand ridges together with the coarsening up in lithology. All of these sedimentary characteristics indicate that shelf sand ridges deposited in the Pearl River Mouth Basin are reconstructed by the tidal and coastal current.

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

Directory of Open Access Journals (Sweden)

Minh Vu Trieu

2017-03-01

Full Text Available This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS, Brazilian tensile strength (BTS, rock brittleness index (BI, the distance between planes of weakness (DPW, and the alpha angle (Alpha between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP. Four (4 statistical regression models (two linear and two nonlinear are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2 of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

Science.gov (United States)

Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

2017-03-01

This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

Deep ensemble learning of sparse regression models for brain disease diagnosis.

Science.gov (United States)

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2017-04-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

A computational approach to compare regression modelling strategies in prediction research.

Science.gov (United States)

Pajouheshnia, Romin; Pestman, Wiebe R; Teerenstra, Steven; Groenwold, Rolf H H

2016-08-25

It is often unclear which approach to fit, assess and adjust a model will yield the most accurate prediction model. We present an extension of an approach for comparing modelling strategies in linear regression to the setting of logistic regression and demonstrate its application in clinical prediction research. A framework for comparing logistic regression modelling strategies by their likelihoods was formulated using a wrapper approach. Five different strategies for modelling, including simple shrinkage methods, were compared in four empirical data sets to illustrate the concept of a priori strategy comparison. Simulations were performed in both randomly generated data and empirical data to investigate the influence of data characteristics on strategy performance. We applied the comparison framework in a case study setting. Optimal strategies were selected based on the results of a priori comparisons in a clinical data set and the performance of models built according to each strategy was assessed using the Brier score and calibration plots. The performance of modelling strategies was highly dependent on the characteristics of the development data in both linear and logistic regression settings. A priori comparisons in four empirical data sets found that no strategy consistently outperformed the others. The percentage of times that a model adjustment strategy outperformed a logistic model ranged from 3.9 to 94.9 %, depending on the strategy and data set. However, in our case study setting the a priori selection of optimal methods did not result in detectable improvement in model performance when assessed in an external data set. The performance of prediction modelling strategies is a data-dependent process and can be highly variable between data sets within the same clinical domain. A priori strategy comparison can be used to determine an optimal logistic regression modelling strategy for a given data set before selecting a final modelling approach.

Interactions Between Mantle Plumes and Mid-Ocean Ridges: Constraints from Geophysics, Geochemistry, and Geodynamical Modeling

National Research Council Canada - National Science Library

Georgen, Jennifer

2001-01-01

This thesis studies interactions between mid-ocean ridges and mantle plumes. Chapter 1 investigates the effects of the Marion and Bouvet hotspots on the ultra-slow spreading, highly-segmented Southwest Indian Ridge (SWIR...

Geology along the Blue Ridge Parkway in Virginia

Science.gov (United States)

Carter, Mark W.; Southworth, C. Scott; Tollo, Richard P.; Merschat, Arthur J.; Wagner, Sara; Lazor, Ava; Aleinikoff, John N.

2017-01-01

Back Formations. These rocks are bound by numerous faults, including the Rock Castle Creek fault that separates Ashe Formation rocks from Alligator Back Formation rocks in the core of the Ararat River synclinorium. The lack of unequivocal paleontologic or geochronologic ages for any of these rock sequences, combined with fundamental and conflicting differences in tectonogenetic models, compound the problem of regional correlation with Blue Ridge cover rocks to the north.The geologic transition from the central to southern Appalachians is also marked by a profound change in landscape and surficial deposits. In central Virginia, the Blue Ridge consists of narrow ridges that are held up by resistant but contrasting basement and cover lithologies. These ridges have shed eroded material from their crests to the base of the mountain fronts in the form of talus slopes, debris flows, and alluvial-colluvial fans for perhaps 10 m.y. South of Roanoke, however, ridges transition into a broad hilly plateau, flanked on the east by the Blue Ridge escarpment and the eastern Continental Divide. Here, deposits of rounded pebbles, cobbles, and boulders preserve remnants of ancestral west-flowing drainage systems.Both bedrock and surficial geologic processes provide an array of economic deposits along the length of the Blue Ridge Parkway corridor in Virginia, including base and precious metals and industrial minerals. However, common stone was the most important commodity for creating the Blue Ridge Parkway, which yielded building stone for overlooks and tunnels, or crushed stone for road base and pavement.

A generalized right truncated bivariate Poisson regression model with applications to health data.

Science.gov (United States)

Islam, M Ataharul; Chowdhury, Rafiqul I

2017-01-01

A generalized right truncated bivariate Poisson regression model is proposed in this paper. Estimation and tests for goodness of fit and over or under dispersion are illustrated for both untruncated and right truncated bivariate Poisson regression models using marginal-conditional approach. Estimation and test procedures are illustrated for bivariate Poisson regression models with applications to Health and Retirement Study data on number of health conditions and the number of health care services utilized. The proposed test statistics are easy to compute and it is evident from the results that the models fit the data very well. A comparison between the right truncated and untruncated bivariate Poisson regression models using the test for nonnested models clearly shows that the truncated model performs significantly better than the untruncated model.

Prediction of Agriculture Drought Using Support Vector Regression Incorporating with Climatology Indices

Science.gov (United States)

Tian, Y.; Xu, Y. P.

2017-12-01

In this paper, the Support Vector Regression (SVR) model incorporating climate indices and drought indices are developed to predict agriculture drought in Xiangjiang River basin, Central China. The agriculture droughts are presented with the Precipitation-Evapotranspiration Index (SPEI). According to the analysis of the relationship between SPEI with different time scales and soil moisture, it is found that SPEI of six months time scales (SPEI-6) could reflect the soil moisture better than that of three and one month time scale from the drought features including drought duration, severity and peak. Climate forcing like El Niño Southern Oscillation and western Pacific subtropical high (WPSH) are represented by climate indices such as MEI and series indices of WPSH. Ridge Point of WPSH is found to be the key factor that influences the agriculture drought mainly through the control of temperature. Based on the climate indices analysis, the predictions of SPEI-6 are conducted using the SVR model. The results show that the SVR model incorperating climate indices, especially ridge point of WPSH, could improve the prediction accuracy compared to that using drought index only. The improvement was more significant for the prediction of one month lead time than that of three months lead time. However, it needs to be cautious in selection of the input parameters, since adding more useless information could have a counter effect in attaining a better prediction.

Improved voltage performance of the Oak Ridge 25URC tandem accelerator

International Nuclear Information System (INIS)

Meigs, M.J.; Jones, C.M.; Haynes, D.L.; Juras, R.C.; Ziegler, N.F.; Roatz, J.E.; Rathmell, R.D.

1989-01-01

This paper reports on the Oak Ridge 25URC tandem electrostatic accelerator one of two accelerators operated by the Holifield Heavy Ion Research Facility (HHIRF) at the Oak Ridge National Laboratory. Placed into routine service in 1982, the accelerator has provided a wide range of heavy ion beams for research in nuclear and atomic physics. These beams have been provided both directly and after further acceleration by the Oak Ridge Isochronous Cyclotron (ORIC). Show schematically in this paper, the tandem accelerator is a model 25URC Pelletron accelerator

Linear regression metamodeling as a tool to summarize and present simulation model results.

Science.gov (United States)

Jalal, Hawre; Dowd, Bryan; Sainfort, François; Kuntz, Karen M

2013-10-01

Modelers lack a tool to systematically and clearly present complex model results, including those from sensitivity analyses. The objective was to propose linear regression metamodeling as a tool to increase transparency of decision analytic models and better communicate their results. We used a simplified cancer cure model to demonstrate our approach. The model computed the lifetime cost and benefit of 3 treatment options for cancer patients. We simulated 10,000 cohorts in a probabilistic sensitivity analysis (PSA) and regressed the model outcomes on the standardized input parameter values in a set of regression analyses. We used the regression coefficients to describe measures of sensitivity analyses, including threshold and parameter sensitivity analyses. We also compared the results of the PSA to deterministic full-factorial and one-factor-at-a-time designs. The regression intercept represented the estimated base-case outcome, and the other coefficients described the relative parameter uncertainty in the model. We defined simple relationships that compute the average and incremental net benefit of each intervention. Metamodeling produced outputs similar to traditional deterministic 1-way or 2-way sensitivity analyses but was more reliable since it used all parameter values. Linear regression metamodeling is a simple, yet powerful, tool that can assist modelers in communicating model characteristics and sensitivity analyses.

Hierarchical Neural Regression Models for Customer Churn Prediction

Directory of Open Access Journals (Sweden)

Golshan Mohammadi

2013-01-01

Full Text Available As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN, self-organizing maps (SOM, alpha-cut fuzzy c-means (α-FCM, and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, and α-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, the α-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.

LINEAR REGRESSION MODEL ESTİMATİON FOR RIGHT CENSORED DATA

Directory of Open Access Journals (Sweden)

Ersin Yılmaz

2016-05-01

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

Developing and testing a global-scale regression model to quantify mean annual streamflow

Science.gov (United States)

Barbarossa, Valerio; Huijbregts, Mark A. J.; Hendriks, A. Jan; Beusen, Arthur H. W.; Clavreul, Julie; King, Henry; Schipper, Aafke M.

2017-01-01

Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF based on a dataset unprecedented in size, using observations of discharge and catchment characteristics from 1885 catchments worldwide, measuring between 2 and 106 km2. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area and catchment averaged mean annual precipitation and air temperature, slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error (RMSE) values were lower (0.29-0.38 compared to 0.49-0.57) and the modified index of agreement (d) was higher (0.80-0.83 compared to 0.72-0.75). Our regression model can be applied globally to estimate MAF at any point of the river network, thus providing a feasible alternative to spatially explicit process-based global hydrological models.

Resolving issues at the Department of Energy/Oak Ridge operations facilities

International Nuclear Information System (INIS)

Row, T.H.; Adams, W.D.

1988-01-01

The development of the US Department of Energy Oak Ridge Operations Office's model for waste management and its application in the Oak Ridge Reservation are discussed. The concept simply stated is to assure that all stakeholders in waste management decisions have the opportunity to be participants from the first step. The paper discusses the advisory committees involved in the process, subcontracting support, college and university relation, technology demonstrations and planning, other federal agency interaction, and the model meeting

Remedial Investigation Work Plan for Chestnut Ridge Operable Unit 1 (Chestnut Ridge Security Pits) at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

Energy Technology Data Exchange (ETDEWEB)

1993-09-01

This Remedial Investigation (RI) Work Plan specifically addresses Chestnut Ridge Operable Unit 1, (OU1) which consists of the Chestnut Ridge Security Pits (CRSP). The CRSP are located {approximately}800 ft southeast of the central portion of the Y-12 Plant atop Chestnut Ridge, which is bounded to the northwest by Bear Creek Valley and to the southeast by Bethel Valley. Operated from 1973 to 1988, the CRSP consisted of a series of trenches used for the disposal of classified hazardous and nonhazardous waste materials. Disposal of hazardous waste materials was discontinued in December 1984, while nonhazardous waste disposal ended on November 8, 1988. An RI is being conducted at this site in response to CERCLA regulations. The overall objectives of the RI are to collect data necessary to evaluate the nature and extent of contaminants of concern (COC), support an ecological risk assessment (ERA) and a human health risk assessment (HHRA), support the evaluation of remedial alternatives, and ultimately develop a Record of Decision for the site. The purpose of this Work Plan is to outline RI activities necessary to define the nature and extent of suspected contaminants at Chestnut Ridge OU1. Potential migration pathways also will be investigated. Data collected during the RI will be used to evaluate the overall risk posed to human health and the environment by OU1.

Remedial Investigation Work Plan for Chestnut Ridge Operable Unit 1 (Chestnut Ridge Security Pits) at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1993-09-01

This Remedial Investigation (RI) Work Plan specifically addresses Chestnut Ridge Operable Unit 1, (OU1) which consists of the Chestnut Ridge Security Pits (CRSP). The CRSP are located ∼800 ft southeast of the central portion of the Y-12 Plant atop Chestnut Ridge, which is bounded to the northwest by Bear Creek Valley and to the southeast by Bethel Valley. Operated from 1973 to 1988, the CRSP consisted of a series of trenches used for the disposal of classified hazardous and nonhazardous waste materials. Disposal of hazardous waste materials was discontinued in December 1984, while nonhazardous waste disposal ended on November 8, 1988. An RI is being conducted at this site in response to CERCLA regulations. The overall objectives of the RI are to collect data necessary to evaluate the nature and extent of contaminants of concern (COC), support an ecological risk assessment (ERA) and a human health risk assessment (HHRA), support the evaluation of remedial alternatives, and ultimately develop a Record of Decision for the site. The purpose of this Work Plan is to outline RI activities necessary to define the nature and extent of suspected contaminants at Chestnut Ridge OU1. Potential migration pathways also will be investigated. Data collected during the RI will be used to evaluate the overall risk posed to human health and the environment by OU1

A land use regression model for ambient ultrafine particles in Montreal, Canada: A comparison of linear regression and a machine learning approach.

Science.gov (United States)

Weichenthal, Scott; Ryswyk, Keith Van; Goldstein, Alon; Bagg, Scott; Shekkarizfard, Maryam; Hatzopoulou, Marianne

2016-04-01

Existing evidence suggests that ambient ultrafine particles (UFPs) (regression model for UFPs in Montreal, Canada using mobile monitoring data collected from 414 road segments during the summer and winter months between 2011 and 2012. Two different approaches were examined for model development including standard multivariable linear regression and a machine learning approach (kernel-based regularized least squares (KRLS)) that learns the functional form of covariate impacts on ambient UFP concentrations from the data. The final models included parameters for population density, ambient temperature and wind speed, land use parameters (park space and open space), length of local roads and rail, and estimated annual average NOx emissions from traffic. The final multivariable linear regression model explained 62% of the spatial variation in ambient UFP concentrations whereas the KRLS model explained 79% of the variance. The KRLS model performed slightly better than the linear regression model when evaluated using an external dataset (R(2)=0.58 vs. 0.55) or a cross-validation procedure (R(2)=0.67 vs. 0.60). In general, our findings suggest that the KRLS approach may offer modest improvements in predictive performance compared to standard multivariable linear regression models used to estimate spatial variations in ambient UFPs. However, differences in predictive performance were not statistically significant when evaluated using the cross-validation procedure. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

Adaptive regression for modeling nonlinear relationships

CERN Document Server

Knafl, George J

2016-01-01

This book presents methods for investigating whether relationships are linear or nonlinear and for adaptively fitting appropriate models when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for different types of outcome variables. Such nonlinear dependence is often not considered in applied research, yet nonlinear relationships are common and so need to be addressed. A standard linear analysis can produce misleading conclusions, while a nonlinear analysis can provide novel insights into data, not otherwise possible. A variety of examples of the benefits of modeling nonlinear relationships are presented throughout the book. Methods are covered using what are called fractional polynomials based on real-valued power transformations of primary predictor variables combined with model selection based on likelihood cross-validation. The book covers how to formulate and conduct such adaptive fractional polynomial modeling in the s...

Confidence bands for inverse regression models

International Nuclear Information System (INIS)

Birke, Melanie; Bissantz, Nicolai; Holzmann, Hajo

2010-01-01

We construct uniform confidence bands for the regression function in inverse, homoscedastic regression models with convolution-type operators. Here, the convolution is between two non-periodic functions on the whole real line rather than between two periodic functions on a compact interval, since the former situation arguably arises more often in applications. First, following Bickel and Rosenblatt (1973 Ann. Stat. 1 1071–95) we construct asymptotic confidence bands which are based on strong approximations and on a limit theorem for the supremum of a stationary Gaussian process. Further, we propose bootstrap confidence bands based on the residual bootstrap and prove consistency of the bootstrap procedure. A simulation study shows that the bootstrap confidence bands perform reasonably well for moderate sample sizes. Finally, we apply our method to data from a gel electrophoresis experiment with genetically engineered neuronal receptor subunits incubated with rat brain extract

Electricity consumption forecasting in Italy using linear regression models

Energy Technology Data Exchange (ETDEWEB)

Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio [DIAM, Seconda Universita degli Studi di Napoli, Via Roma 29, 81031 Aversa (CE) (Italy)

2009-09-15

The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of {+-}1% for the best case and {+-}11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

Electricity consumption forecasting in Italy using linear regression models

International Nuclear Information System (INIS)

Bianco, Vincenzo; Manca, Oronzio; Nardini, Sergio

2009-01-01

The influence of economic and demographic variables on the annual electricity consumption in Italy has been investigated with the intention to develop a long-term consumption forecasting model. The time period considered for the historical data is from 1970 to 2007. Different regression models were developed, using historical electricity consumption, gross domestic product (GDP), gross domestic product per capita (GDP per capita) and population. A first part of the paper considers the estimation of GDP, price and GDP per capita elasticities of domestic and non-domestic electricity consumption. The domestic and non-domestic short run price elasticities are found to be both approximately equal to -0.06, while long run elasticities are equal to -0.24 and -0.09, respectively. On the contrary, the elasticities of GDP and GDP per capita present higher values. In the second part of the paper, different regression models, based on co-integrated or stationary data, are presented. Different statistical tests are employed to check the validity of the proposed models. A comparison with national forecasts, based on complex econometric models, such as Markal-Time, was performed, showing that the developed regressions are congruent with the official projections, with deviations of ±1% for the best case and ±11% for the worst. These deviations are to be considered acceptable in relation to the time span taken into account. (author)

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Directory of Open Access Journals (Sweden)

Drzewiecki Wojciech

2016-12-01

Full Text Available In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques.

Preliminary results from the first InRidge cruise to the central Indian Ridge

Digital Repository Service at National Institute of Oceanography (India)

Mukhopadhyay, R.; Murthy, K.S.R.; Iyer, S.D.; Rao, M.M.M.; Banerjee, R.; Subrahmanyam, A.S.; Shirodkar, P.V.; Ghose, I.; Ganesan, P.; Rao, A.K.; Suribabu, A.; Ganesh, C.; Naik, G.P.

stream_size 1 stream_content_type text/plain stream_name Inter_Ridge_News_7_40.pdf.txt stream_source_info Inter_Ridge_News_7_40.pdf.txt Content-Encoding ISO-8859-1 Content-Type text/plain; charset=ISO-8859-1 ...

Modelling infant mortality rate in Central Java, Indonesia use generalized poisson regression method

Science.gov (United States)

Prahutama, Alan; Sudarno

2018-05-01

The infant mortality rate is the number of deaths under one year of age occurring among the live births in a given geographical area during a given year, per 1,000 live births occurring among the population of the given geographical area during the same year. This problem needs to be addressed because it is an important element of a country’s economic development. High infant mortality rate will disrupt the stability of a country as it relates to the sustainability of the population in the country. One of regression model that can be used to analyze the relationship between dependent variable Y in the form of discrete data and independent variable X is Poisson regression model. Recently The regression modeling used for data with dependent variable is discrete, among others, poisson regression, negative binomial regression and generalized poisson regression. In this research, generalized poisson regression modeling gives better AIC value than poisson regression. The most significant variable is the Number of health facilities (X1), while the variable that gives the most influence to infant mortality rate is the average breastfeeding (X9).

Augmented Beta rectangular regression models: A Bayesian perspective.

Science.gov (United States)

Wang, Jue; Luo, Sheng

2016-01-01

Mixed effects Beta regression models based on Beta distributions have been widely used to analyze longitudinal percentage or proportional data ranging between zero and one. However, Beta distributions are not flexible to extreme outliers or excessive events around tail areas, and they do not account for the presence of the boundary values zeros and ones because these values are not in the support of the Beta distributions. To address these issues, we propose a mixed effects model using Beta rectangular distribution and augment it with the probabilities of zero and one. We conduct extensive simulation studies to assess the performance of mixed effects models based on both the Beta and Beta rectangular distributions under various scenarios. The simulation studies suggest that the regression models based on Beta rectangular distributions improve the accuracy of parameter estimates in the presence of outliers and heavy tails. The proposed models are applied to the motivating Neuroprotection Exploratory Trials in Parkinson's Disease (PD) Long-term Study-1 (LS-1 study, n = 1741), developed by The National Institute of Neurological Disorders and Stroke Exploratory Trials in Parkinson's Disease (NINDS NET-PD) network. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bayesian semiparametric regression models to characterize molecular evolution

Directory of Open Access Journals (Sweden)

Datta Saheli

2012-10-01

Full Text Available Abstract Background Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties. We propose a Bayesian hierarchical regression model with a generalization of the Dirichlet process prior on the distribution of the regression coefficients that describes the relationship between the changes in amino acid distances and natural selection in protein-coding DNA sequence alignments. Results The Bayesian semiparametric approach is illustrated with simulated data and the abalone lysin sperm data. Our method identifies groups of properties which, for this particular dataset, have a similar effect on evolution. The model also provides nonparametric site-specific estimates for the strength of conservation of these properties. Conclusions The model described here is distinguished by its ability to handle a large number of amino acid properties simultaneously, while taking into account that such data can be correlated. The multi-level clustering ability of the model allows for appealing interpretations of the results in terms of properties that are roughly equivalent from the standpoint of molecular evolution.

Regression analysis of a chemical reaction fouling model

International Nuclear Information System (INIS)

Vasak, F.; Epstein, N.

1996-01-01

A previously reported mathematical model for the initial chemical reaction fouling of a heated tube is critically examined in the light of the experimental data for which it was developed. A regression analysis of the model with respect to that data shows that the reference point upon which the two adjustable parameters of the model were originally based was well chosen, albeit fortuitously. (author). 3 refs., 2 tabs., 2 figs

Correlation-regression model for physico-chemical quality of ...

African Journals Online (AJOL)

abusaad

areas, suggesting that groundwater quality in urban areas is closely related with land use ... the ground water, with correlation and regression model is also presented. ...... WHO (World Health Organization) (1985). Health hazards from nitrates.

Greenland Fracture Zone-East Greenland Ridge(s) revisited: Indications of a C22-change in plate motion?

DEFF Research Database (Denmark)

Døssing, Arne; Funck, T.

2012-01-01

a reinterpretation of the Greenland Fracture Zone -East Greenland Ridge based on new and existing geophysical data. Evidence is shown for two overstepping ridge segments (Segments A and B) of which Segment A corresponds to the already known East Greenland Ridge while Segment B was not detected previously......Changes in the lithospheric stress field, causing axial rift migration and reorientation of the transform, are generally proposed as an explanation for anomalously old crust and/or major aseismic valleys in oceanic ridge-transform-ridge settings. Similarly, transform migration of the Greenland...... Fracture Zone and separation of the 200-km-long, fracture-zone-parallel continental East Greenland Ridge from the Eurasia plate is thought to be related to a major change in relative plate motions between Greenland and Eurasia during the earliest Oligocene (Chron 13 time). This study presents...

Multiple linear regression and regression with time series error models in forecasting PM10 concentrations in Peninsular Malaysia.

Science.gov (United States)

Ng, Kar Yong; Awang, Norhashidah

2018-01-06

Frequent haze occurrences in Malaysia have made the management of PM 10 (particulate matter with aerodynamic less than 10 μm) pollution a critical task. This requires knowledge on factors associating with PM 10 variation and good forecast of PM 10 concentrations. Hence, this paper demonstrates the prediction of 1-day-ahead daily average PM 10 concentrations based on predictor variables including meteorological parameters and gaseous pollutants. Three different models were built. They were multiple linear regression (MLR) model with lagged predictor variables (MLR1), MLR model with lagged predictor variables and PM 10 concentrations (MLR2) and regression with time series error (RTSE) model. The findings revealed that humidity, temperature, wind speed, wind direction, carbon monoxide and ozone were the main factors explaining the PM 10 variation in Peninsular Malaysia. Comparison among the three models showed that MLR2 model was on a same level with RTSE model in terms of forecasting accuracy, while MLR1 model was the worst.

Multivariate Frequency-Severity Regression Models in Insurance

Directory of Open Access Journals (Sweden)

Edward W. Frees

2016-02-01

Full Text Available In insurance and related industries including healthcare, it is common to have several outcome measures that the analyst wishes to understand using explanatory variables. For example, in automobile insurance, an accident may result in payments for damage to one’s own vehicle, damage to another party’s vehicle, or personal injury. It is also common to be interested in the frequency of accidents in addition to the severity of the claim amounts. This paper synthesizes and extends the literature on multivariate frequency-severity regression modeling with a focus on insurance industry applications. Regression models for understanding the distribution of each outcome continue to be developed yet there now exists a solid body of literature for the marginal outcomes. This paper contributes to this body of literature by focusing on the use of a copula for modeling the dependence among these outcomes; a major advantage of this tool is that it preserves the body of work established for marginal models. We illustrate this approach using data from the Wisconsin Local Government Property Insurance Fund. This fund offers insurance protection for (i property; (ii motor vehicle; and (iii contractors’ equipment claims. In addition to several claim types and frequency-severity components, outcomes can be further categorized by time and space, requiring complex dependency modeling. We find significant dependencies for these data; specifically, we find that dependencies among lines are stronger than the dependencies between the frequency and average severity within each line.

Quantile Regression Methods

DEFF Research Database (Denmark)

Fitzenberger, Bernd; Wilke, Ralf Andreas

2015-01-01

if the mean regression model does not. We provide a short informal introduction into the principle of quantile regression which includes an illustrative application from empirical labor market research. This is followed by briefly sketching the underlying statistical model for linear quantile regression based......Quantile regression is emerging as a popular statistical approach, which complements the estimation of conditional mean models. While the latter only focuses on one aspect of the conditional distribution of the dependent variable, the mean, quantile regression provides more detailed insights...... by modeling conditional quantiles. Quantile regression can therefore detect whether the partial effect of a regressor on the conditional quantiles is the same for all quantiles or differs across quantiles. Quantile regression can provide evidence for a statistical relationship between two variables even...

A linked land-sea modeling framework to inform ridge-to-reef management in high oceanic islands.

Directory of Open Access Journals (Sweden)

Jade M S Delevaux

Full Text Available Declining natural resources have led to a cultural renaissance across the Pacific that seeks to revive customary ridge-to-reef management approaches to protect freshwater and restore abundant coral reef fisheries. Effective ridge-to-reef management requires improved understanding of land-sea linkages and decision-support tools to simultaneously evaluate the effects of terrestrial and marine drivers on coral reefs, mediated by anthropogenic activities. Although a few applications have linked the effects of land cover to coral reefs, these are too coarse in resolution to inform watershed-scale management for Pacific Islands. To address this gap, we developed a novel linked land-sea modeling framework based on local data, which coupled groundwater and coral reef models at fine spatial resolution, to determine the effects of terrestrial drivers (groundwater and nutrients, mediated by human activities (land cover/use, and marine drivers (waves, geography, and habitat on coral reefs. We applied this framework in two 'ridge-to-reef' systems (Hā'ena and Ka'ūpūlehu subject to different natural disturbance regimes, located in the Hawaiian Archipelago. Our results indicated that coral reefs in Ka'ūpūlehu are coral-dominated with many grazers and scrapers due to low rainfall and wave power. While coral reefs in Hā'ena are dominated by crustose coralline algae with many grazers and less scrapers due to high rainfall and wave power. In general, Ka'ūpūlehu is more vulnerable to land-based nutrients and coral bleaching than Hā'ena due to high coral cover and limited dilution and mixing from low rainfall and wave power. However, the shallow and wave sheltered back-reef areas of Hā'ena, which support high coral cover and act as nursery habitat for fishes, are also vulnerable to land-based nutrients and coral bleaching. Anthropogenic sources of nutrients located upstream from these vulnerable areas are relevant locations for nutrient mitigation, such as

Application of random regression models to the genetic evaluation ...

African Journals Online (AJOL)

The model included fixed regression on AM (range from 30 to 138 mo) and the effect of herd-measurement date concatenation. Random parts of the model were RRM coefficients for additive and permanent environmental effects, while residual effects were modelled to account for heterogeneity of variance by AY. Estimates ...

Multitask Quantile Regression under the Transnormal Model.

Science.gov (United States)

Fan, Jianqing; Xue, Lingzhou; Zou, Hui

2016-01-01

We consider estimating multi-task quantile regression under the transnormal model, with focus on high-dimensional setting. We derive a surprisingly simple closed-form solution through rank-based covariance regularization. In particular, we propose the rank-based ℓ 1 penalization with positive definite constraints for estimating sparse covariance matrices, and the rank-based banded Cholesky decomposition regularization for estimating banded precision matrices. By taking advantage of alternating direction method of multipliers, nearest correlation matrix projection is introduced that inherits sampling properties of the unprojected one. Our work combines strengths of quantile regression and rank-based covariance regularization to simultaneously deal with nonlinearity and nonnormality for high-dimensional regression. Furthermore, the proposed method strikes a good balance between robustness and efficiency, achieves the "oracle"-like convergence rate, and provides the provable prediction interval under the high-dimensional setting. The finite-sample performance of the proposed method is also examined. The performance of our proposed rank-based method is demonstrated in a real application to analyze the protein mass spectroscopy data.

Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models

Directory of Open Access Journals (Sweden)

Jiefang Dong

2016-12-01

Full Text Available With the rapid economic development of the Xinjiang Uygur Autonomous Region, the area’s transport sector has witnessed significant growth, which in turn has led to a large increase in carbon dioxide emissions. As such, calculating of the carbon footprint of Xinjiang’s transportation sector and probing the driving factors of carbon dioxide emissions are of great significance to the region’s energy conservation and environmental protection. This paper provides an account of the growth in the carbon emissions of Xinjiang’s transportation sector during the period from 1989 to 2012. We also analyze the transportation sector’s trends and historical evolution. Combined with the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology model and ridge regression, this study further quantitatively analyzes the factors that influence the carbon emissions of Xinjiang’s transportation sector. The results indicate the following: (1 the total carbon emissions and per capita carbon emissions of Xinjiang’s transportation sector both continued to rise rapidly during this period; their average annual growth rates were 10.8% and 9.1%, respectively; (2 the carbon emissions of the transportation sector come mainly from the consumption of diesel and gasoline, which accounted for an average of 36.2% and 2.6% of carbon emissions, respectively; in addition, the overall carbon emission intensity of the transportation sector showed an “S”-pattern trend within the study period; (3 population density plays a dominant role in increasing carbon dioxide emissions. Population is then followed by per capita GDP and, finally, energy intensity. Cargo turnover has a more significant potential impact on and role in emission reduction than do private vehicles. This is because road freight is the primary form of transportation used across Xinjiang, and this form of transportation has low energy efficiency. These findings have important

Approximating prediction uncertainty for random forest regression models

Science.gov (United States)

John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

2016-01-01

Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

Alveolar ridge augmentation by osteoinduction in rats

DEFF Research Database (Denmark)

Pinholt, E M; Bang, G; Haanaes, H R

1990-01-01

The purpose of this study was to evaluate bone substitutes for alveolar ridge augmentation by osteoinduction. Allogenic, demineralized, and lyophilized dentin and bone was tested for osteoinductive properties in order to establish an experimental model for further studies. Implantations were perf...

Global survey of lunar wrinkle ridge formation times

Science.gov (United States)

Yue, Z.; Michael, G. G.; Di, K.; Liu, J.

2017-11-01

Wrinkle ridges are a common feature of the lunar maria and record subsequent contraction of mare infill. Constraining the timing of wrinkle ridge formation from crater counts is challenging because they have limited areal extent and it is difficult to determine whether superposed craters post-date ridge formation or have alternatively been uplifted by the deformation. Some wrinkle ridges do allow determination to be made. This is possible where a ridge shows a sufficiently steep boundary or scarp that can be identified as deforming an intersecting crater or the crater obliterates the relief of the ridge. Such boundaries constitute only a small fraction of lunar wrinkle ridge structures yet they are sufficiently numerous to enable us to obtain statistically significant crater counts over systems of structurally related wrinkle ridges. We carried out a global mapping of mare wrinkle ridges, identifying appropriate boundaries for crater identification, and mapping superposed craters. Selected groups of ridges were analyzed using the buffered crater counting method. We found that, except for the ridges in mare Tranquilitatis, the ridge groups formed with average ages between 3.5 and 3.1 Ga ago, or 100-650 Ma after the oldest observable erupted basalts where they are located. We interpret these results to suggest that local stresses from loading by basalt fill are the principal agent responsible for the formation of lunar wrinkle ridges, as others have proposed. We find a markedly longer interval before wrinkle ridge formation in Tranquilitatis which likely indicates a different mechanism of stress accumulation at this site.

Hot subduction: Magmatism along the Hunter Ridge, SW Pacific

International Nuclear Information System (INIS)

Crawford, A.J.; Verbeeten, A.; Danyushevsky, L.V.; Sigurdsson, I.A.; Maillet, P.; Monzier, M.

1997-01-01

The Hunter 'fracture zone' is generally regarded as a transform plate boundary linking the oppositely dipping Tongan and Vanuatu subduction systems. Dredging along the Hunter Ridge and sampling of its northernmost extent, exposed as the island of Kadavu in Fiji, has yielded a diversity of magmatic suites, including arc tholeiites and high-Ca boninites, high-Mg lavas with some affinities to boninites and some affinities to adakites, and true adakitic lavas associated with remarkable low-Fe, high-Na basalts with 8-16 ppm Nb (herein high-Nb basalts). Lavas which show clear evidence of slab melt involvement in their petrogenesis occur at either end of the Hunter Ridge, whereas the arc tholeiites and high-Ca boninites appear to be restricted to the south central part of the ridge. Mineralogical and whole rock geochemical data for each of these suites are summarized, and a tectono-magmatic model for their genesis and distribution is suggested. Trace element features and radiogenic isotope data for the Hunter Ridge lavas indicate compositions analogue to Pacific MORB-like mantle

Profile-driven regression for modeling and runtime optimization of mobile networks

DEFF Research Database (Denmark)

McClary, Dan; Syrotiuk, Violet; Kulahci, Murat

2010-01-01

Computer networks often display nonlinear behavior when examined over a wide range of operating conditions. There are few strategies available for modeling such behavior and optimizing such systems as they run. Profile-driven regression is developed and applied to modeling and runtime optimization...... of throughput in a mobile ad hoc network, a self-organizing collection of mobile wireless nodes without any fixed infrastructure. The intermediate models generated in profile-driven regression are used to fit an overall model of throughput, and are also used to optimize controllable factors at runtime. Unlike...

Best management practices plan for the Chestnut Ridge-Filled Coal Ash Pond at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1996-05-01

The Chestnut Ridge Filled Coal Ash Pond (FCAP) Project has been established to satisfy Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA) requirements for the Chestnut Ridge Operable Unit 2. FCAP is on Chestnut Ridge, approximately 0.5 miles south of the Y-12 Plant. A 62-foot high earthen dam across Upper McCoy Branch was constructed in 1955 to create a pond to serve as a settling basin for fly and bottom ashes generated by burning coal at the Y-12 Steam Plant. Ash from the steam was mixed with water to form a slurry and then pumped to the crest of Chestnut Ridge and released through a large pipe to flow across the Sluice Channel area and into the pond. The ash slurry eventually overtopped the dam and flowed along Upper McCoy Branch to Rogers Quarry. The purpose of this document is to provide a site-specific Best Management Practices (BMP) Plan for construction associated with environmental restoration activities at the FCAP Site

Magnetic Anomalies over the Mid-Atlantic Ridge near 27{degrees}N.

Science.gov (United States)

Phillips, J D

1967-08-25

Ten magnetic profiles across the mid-Atlantic ridge near 27 degrees N show trends that are parallel to the ridge axis and symmetrical about the ridge axis. The configuration of magnetic bodies that could account for the pattern supports the Vine and Matthews hypothesis for the origin of magnetic anomalies over oceanic ridges. A polarity-reversal time scale inferred from models for sea-floor spreading in the Pacific-Antarctic ridge and radiometrically dated reversals of the geomagnetic field indicates a spreading rate of 1.25 centimeters per year during the last 6 million years and a rate of 1.65 centimeters per year between 6 and 10 million years ago. A similar analysis of more limited data over the mid-Atlantic ridge near 22 degrees N also indicates a change in the spreading rate. Here a rate of 1.4 centimeters per year appears to have been in effect during the last 5 million years; between 5 and 9 million years ago, an increased rate of 1.7 centimeters per year is indicated. The time of occurrence and relative magnitude of these changes in the spreading rate, about 5 to 6 million years ago and 18 to 27 percent, respectively, accords with the spreading rate change implied for the Juan de Fuca ridge in the northeast Pacific.

Ridge interaction features of the Line Islands

Science.gov (United States)

Konter, J. G.; Koppers, A. A. P.; Storm, L. P.

2016-12-01

The sections of Pacific absolute plate motion history that precede the Hawaii-Emperor and Louisville chains are based on three chains: the Line Islands-Mid-Pacific Mountains, the Hess Rise-Shatsky Rise, and the Marshall Islands-Wake Islands (Rurutu hotspot). Although it has been clear that the Line Islands do not define a simple age progression (e.g. Schlanger et al., 1984), the apparent similarity to the Emperor Seamount geographic trend has been used to extend the overall Hawaii-Emperor track further into the past. However, we show here that plate tectonic reconstructions suggest that the Mid-Pacific Mountains (MPMs) and Line Islands (LIs) were erupted near a mid-ocean ridge, and thus these structures do not reflect absolute plate motion. Moverover, the morphology and geochemistry of the volcanoes show similarities with Pukapuka Ridge (e.g. Davis et al., 2002) and the Rano Rahi seamounts, presumed to have a shallow origin. Modern 40Ar/39Ar ages show that the LIs erupted at various times along the entire volcanic chain. The oldest structures formed within 10 Ma of plate formation. Given the short distance to the ridge system, large aseismic volcanic ridges, such as Necker Ridge and Horizon Guyot may simply reflect a connection between MPMs and the ridge, similar to the Pukapuka Ridge. The Line Islands to the south (including Karin Ridge) define short subchains of elongated seamounts that are widespread, resembling the Rano Rahi seamount field. During this time, the plate moved nearly parallel to the ridge system. The change from few large ridges to many subchains may reflect a change in absolute plate motion, similar to the Rano Rahi field. Here, significant MPMs volcanism is no longer connected to the ridge along plate motion. Similar to Pukapuka vs. Rano Rahi, the difference in direction between plate motion and the closest ridge determines whether larger ridges or smaller seamount subchains are formed. The difference between the largest structures (MPMs and LIs

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

Science.gov (United States)

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

2018-01-01

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

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

Directory of Open Access Journals (Sweden)

Robert Richardson

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

Reconstructing paleo-discharge from geometries of fluvial sinuous ridges on Earth and Mars

Science.gov (United States)

Hayden, A.; Lamb, M. P.; Mohrig, D. C.; Williams, R. M. E.; Myrow, P.; Ewing, R. C.; Cardenas, B. T.; Findlay, C. P., III

2017-12-01

Sinuous, branching networks of topographic ridges resembling river networks are common across Mars, and show promise for quantifying ancient martian surface hydrology. There are two leading formation mechanisms for ridges with a fluvial origin. Inverted channels are ridges that represent casts (e.g., due to lava fill) of relict river channel topography, whereas exhumed channel deposits are eroded remnants of a more extensive fluvial deposit, such as a channel belt. The inverted channel model is often assumed on Mars; however, we currently lack the ability to distinguish these ridge formation mechanisms, motivating the need for Earth-analog study. To address this issue, we studied the extensive networks of sinuous ridges in the Ebro basin of northeast Spain. The Ebro ridges stand 3-15 meters above the surrounding plains and are capped by a cliff-forming sandstone unit 3-10 meters thick and 20-50 meters in breadth. The caprock sandstone bodies contain bar-scale cross stratification, point-bar deposits, levee deposits, and lenses of mudstone, indicating that these are channel-belt deposits, rather than casts of channels formed from lateral channel migration, avulsion and reoccupation. In plan view, ridges form segments branching outward to the north resembling a distributary network; however, crosscutting relationships indicate that ridges cross at different stratigraphic levels. Thus, the apparent network in planview reflects non-uniform exhumation of channel-belt deposits from multiple stratigraphic positions, rather than an inverted coeval river network. As compared to the inverted channel model, exhumed fluvial deposits indicate persistent fluvial activity over geologic timescales, indicating the potential for long-lived surface water on ancient Mars.

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis.

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Vajargah, Kianoush Fathi; Sadeghi-Bazargani, Homayoun; Mehdizadeh-Esfanjani, Robab; Savadi-Oskouei, Daryoush; Farhoudi, Mehdi

2012-01-01

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

Science.gov (United States)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

Sandy berm and beach-ridge formation in relation to extreme sea-levels

DEFF Research Database (Denmark)

Bendixen, Mette; Clemmensen, Lars B; Kroon, Aart

2013-01-01

The formation of berms and their transformation into beach ridges in a micro-tidal environment is coupled to wave run-up and overtopping during extreme sea levels. A straight-forward comparison between extreme sea levels due to storm-surges and active berm levels is impossible in the semi...... prograding spit on the south-eastern Baltic shores of Zealand, Denmark. The modern, sandy beach at this location consists of a beachface with a shallow incipient berm, a mature berm, and a dune-covered beach ridge. It borders a beach-ridge plain to the west, where more than 20 N–S oriented beach ridges...... and swales are present. Measured water-level data from 1991 to 2012 and topographical observations, carried out during fair weather period and during a storm event, provided the basis for a conceptual model exhibiting berm formation and transformation into the local beach-ridge system. The character...

Radiogenic isotopes in enriched mid-ocean ridge basalts from Explorer Ridge, northeast Pacific Ocean

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Cousens, Brian; Weis, Dominique; Constantin, Marc; Scott, Steve

2017-09-01

Extreme gradients in topography related to variations in magma supply are observed on the Southern Explorer Ridge (SER), part of the northern Juan de Fuca ridge system. We report radiogenic isotope (Pb, Sr, Nd, Hf) and geochemical data for twenty-four basalt whole-rock and glass samples collected from the length of the SER and from Explorer Deep, a rift to the north of the SER. Lavas from the SER form a north-south geochemical gradient, dominated by E-MORB at the northern axial high, and range from T-MORB to N-MORB towards the southern deepest part of the ridge. Linear relationships between incompatible element ratios and isotopic ratios in MORB along the ridge are consistent with mixing of magmas beneath the ridge to generate the geographic gradient from E- to N-MORB. The E-MORB have high Sr and Pb, and low Nd and Hf isotopic ratios, typical of enriched mantle that includes a FOZO or HIMU isotopic component. The West Valley and Endeavour segments of the northern Juan de Fuca ridge also include this isotopic component, but the proportion of the FOZO or HIMU component is more extreme in the SER basalts. The FOZO or HIMU component may be garnet-bearing peridotite, or a garnet pyroxenite embedded in peridotite. Recycled garnet pyroxenite better explains the very shallow SER axial high, high Nb/La and La/Sm, and the ;enriched; isotopic compositions.

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

DEFF Research Database (Denmark)

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

2017-01-01

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

A holistic model for the role of the axial melt lens at fast-spreading mid-ocean ridges

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MacLeod, C. J.; Loocke, M. P.; Lissenberg, J. C. J.

2016-12-01

Axial melt lenses (AML) are melt or crystal mush1 bodies located at the dyke-gabbro transition beneath intermediate- and fast-spreading mid-ocean ridges (MORs)2,3. Although it is generally thought that AMLs play a major role in the storage and differentiation of mid-ocean ridge basalts (MORB)1, the melt compositions within the AML and its role in the accretion of the lower crust are heavily debated4-6. Here we present the first comprehensive study of the AML horizon at a fast-spreading MOR (Hess Deep, equatorial Pacific Ocean). We show that plagioclase and pyroxene within the AML are much too evolved to be in equilibrium with MORB, with mean An (54.85) and Mg# (65.01) consistent with derivation from basaltic andesite to andesite melts (Mg# 43-26). We propose that, in between decadal eruptions, the AML is predominantly crystal mush and is fed by small volumes of evolved interstitial melts. Short-lived, focused injection of primitive melt leads to mixing of primitive melts with the extant highly fractionated melt, and triggers eruptions. This model reconciles the paradoxical compositional mismatch between the volcanic and plutonic records with the geophysical characteristics of the AML, the short residence times of Pacific MORB phenocrysts, and the incompatible trace element over-enrichments in MORB. 1Marjanović, M. et al., 2015. Distribution of melt along the East Pacific Rise from 9°30' to 10°N from an amplitude variation with angle of incidence (AVA) technique. Geophys. J. Int. 203. 2Detrick, R. S. et al., 1987. Multi-channel seismic imaging of a crustal magma chamber along the EPR. Nature 326. 3Sinton, J. M. & Detrick, R. S., 1992. Mid-ocean ridge magma chambers. J. Geophys. Res. 97. 4Coogan, L. A., Thompson, G. & MacLeod, C. J., 2002. A textural and geochemical investigation of high level gabbros from the Oman ophiolite: implications for the role of the axial magma chamber at fast-spreading ridges. Lithos 63. 5Pan, Y. & Batiza, R., 2002. Mid-ocean ridge magma

SPSS macros to compare any two fitted values from a regression model.

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Weaver, Bruce; Dubois, Sacha

2012-12-01

In regression models with first-order terms only, the coefficient for a given variable is typically interpreted as the change in the fitted value of Y for a one-unit increase in that variable, with all other variables held constant. Therefore, each regression coefficient represents the difference between two fitted values of Y. But the coefficients represent only a fraction of the possible fitted value comparisons that might be of interest to researchers. For many fitted value comparisons that are not captured by any of the regression coefficients, common statistical software packages do not provide the standard errors needed to compute confidence intervals or carry out statistical tests-particularly in more complex models that include interactions, polynomial terms, or regression splines. We describe two SPSS macros that implement a matrix algebra method for comparing any two fitted values from a regression model. The !OLScomp and !MLEcomp macros are for use with models fitted via ordinary least squares and maximum likelihood estimation, respectively. The output from the macros includes the standard error of the difference between the two fitted values, a 95% confidence interval for the difference, and a corresponding statistical test with its p-value.

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

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Amalia, Junita; Purhadi, Otok, Bambang Widjanarko

2017-11-01

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

Can We Use Regression Modeling to Quantify Mean Annual Streamflow at a Global-Scale?

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Barbarossa, V.; Huijbregts, M. A. J.; Hendriks, J. A.; Beusen, A.; Clavreul, J.; King, H.; Schipper, A.

2016-12-01

Quantifying mean annual flow of rivers (MAF) at ungauged sites is essential for a number of applications, including assessments of global water supply, ecosystem integrity and water footprints. MAF can be quantified with spatially explicit process-based models, which might be overly time-consuming and data-intensive for this purpose, or with empirical regression models that predict MAF based on climate and catchment characteristics. Yet, regression models have mostly been developed at a regional scale and the extent to which they can be extrapolated to other regions is not known. In this study, we developed a global-scale regression model for MAF using observations of discharge and catchment characteristics from 1,885 catchments worldwide, ranging from 2 to 106 km2 in size. In addition, we compared the performance of the regression model with the predictive ability of the spatially explicit global hydrological model PCR-GLOBWB [van Beek et al., 2011] by comparing results from both models to independent measurements. We obtained a regression model explaining 89% of the variance in MAF based on catchment area, mean annual precipitation and air temperature, average slope and elevation. The regression model performed better than PCR-GLOBWB for the prediction of MAF, as root-mean-square error values were lower (0.29 - 0.38 compared to 0.49 - 0.57) and the modified index of agreement was higher (0.80 - 0.83 compared to 0.72 - 0.75). Our regression model can be applied globally at any point of the river network, provided that the input parameters are within the range of values employed in the calibration of the model. The performance is reduced for water scarce regions and further research should focus on improving such an aspect for regression-based global hydrological models.

Modeling Governance KB with CATPCA to Overcome Multicollinearity in the Logistic Regression

Science.gov (United States)

Khikmah, L.; Wijayanto, H.; Syafitri, U. D.

2017-04-01

The problem often encounters in logistic regression modeling are multicollinearity problems. Data that have multicollinearity between explanatory variables with the result in the estimation of parameters to be bias. Besides, the multicollinearity will result in error in the classification. In general, to overcome multicollinearity in regression used stepwise regression. They are also another method to overcome multicollinearity which involves all variable for prediction. That is Principal Component Analysis (PCA). However, classical PCA in only for numeric data. Its data are categorical, one method to solve the problems is Categorical Principal Component Analysis (CATPCA). Data were used in this research were a part of data Demographic and Population Survey Indonesia (IDHS) 2012. This research focuses on the characteristic of women of using the contraceptive methods. Classification results evaluated using Area Under Curve (AUC) values. The higher the AUC value, the better. Based on AUC values, the classification of the contraceptive method using stepwise method (58.66%) is better than the logistic regression model (57.39%) and CATPCA (57.39%). Evaluation of the results of logistic regression using sensitivity, shows the opposite where CATPCA method (99.79%) is better than logistic regression method (92.43%) and stepwise (92.05%). Therefore in this study focuses on major class classification (using a contraceptive method), then the selected model is CATPCA because it can raise the level of the major class model accuracy.

Time series modeling by a regression approach based on a latent process.

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Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice

2009-01-01

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.

Regression to Causality : Regression-style presentation influences causal attribution

DEFF Research Database (Denmark)

Bordacconi, Mats Joe; Larsen, Martin Vinæs

2014-01-01

of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

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

Science.gov (United States)

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

2016-01-01

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

Regression Model to Predict Global Solar Irradiance in Malaysia

Directory of Open Access Journals (Sweden)

Hairuniza Ahmed Kutty

2015-01-01

Full Text Available A novel regression model is developed to estimate the monthly global solar irradiance in Malaysia. The model is developed based on different available meteorological parameters, including temperature, cloud cover, rain precipitate, relative humidity, wind speed, pressure, and gust speed, by implementing regression analysis. This paper reports on the details of the analysis of the effect of each prediction parameter to identify the parameters that are relevant to estimating global solar irradiance. In addition, the proposed model is compared in terms of the root mean square error (RMSE, mean bias error (MBE, and the coefficient of determination (R2 with other models available from literature studies. Seven models based on single parameters (PM1 to PM7 and five multiple-parameter models (PM7 to PM12 are proposed. The new models perform well, with RMSE ranging from 0.429% to 1.774%, R2 ranging from 0.942 to 0.992, and MBE ranging from −0.1571% to 0.6025%. In general, cloud cover significantly affects the estimation of global solar irradiance. However, cloud cover in Malaysia lacks sufficient influence when included into multiple-parameter models although it performs fairly well in single-parameter prediction models.

Polynomial regression analysis and significance test of the regression function

International Nuclear Information System (INIS)

Gao Zhengming; Zhao Juan; He Shengping

2012-01-01

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

Effects of sea level rise on the formation and drowning of shoreface-connected sand ridges, a model study

NARCIS (Netherlands)

Nnafie, A.|info:eu-repo/dai/nl/37551127X; de Swart, Huib|info:eu-repo/dai/nl/073449725; Calvete, D.|info:eu-repo/dai/nl/304846317; Garnier, R.

2014-01-01

Shoreface-connected sand ridges occur on many storm-dominated inner shelves. These rhythmic features have an along-shelf spacing of 2-10. km, a height of 1-12. m, they evolve on timescales of centuries and they migrate several meters per year. An idealized model is used to study the impact of sea

Waveform modeling of the seismic response of a mid-ocean ridge axial melt sill

Science.gov (United States)

Xu, Min; Stephen, R. A.; Canales, J. Pablo

2017-12-01

Seismic reflections from axial magma lens (AML) are commonly observed along many mid-ocean ridges, and are thought to arise from the negative impedance contrast between a solid, high-speed lid and the underlying low-speed, molten or partially molten (mush) sill. The polarity of the AML reflection ( P AML P) at vertical incidence and the amplitude vs offset (AVO) behavior of the AML reflections (e.g., P AML P and S-converted P AML S waves) are often used as a diagnostic tool for the nature of the low-speed sill. Time-domain finite difference calculations for two-dimensional laterally homogeneous models show some scenarios make the interpretation of melt content from partial-offset stacks of P- and S-waves difficult. Laterally heterogeneous model calculations indicate diffractions from the edges of the finite-width AML reducing the amplitude of the AML reflections. Rough seafloor and/or a rough AML surface can also greatly reduce the amplitude of peg-leg multiples because of scattering and destructive interference. Mid-crustal seismic reflection events are observed in the three-dimensional multi-channel seismic dataset acquired over the RIDGE-2000 Integrated Study Site at East Pacific Rise (EPR, cruise MGL0812). Modeling indicates that the mid-crustal seismic reflection reflections are unlikely to arise from peg-leg multiples of the AML reflections, P-to- S converted phases, or scattering due to rough topography, but could probably arise from deeper multiple magma sills. Our results support the identification of Marjanović et al. (Nat Geosci 7(11):825-829, 2014) that a multi-level complex of melt lenses is present beneath the axis of the EPR.

Logistic regression for risk factor modelling in stuttering research.

Science.gov (United States)

Reed, Phil; Wu, Yaqionq

2013-06-01

To outline the uses of logistic regression and other statistical methods for risk factor analysis in the context of research on stuttering. The principles underlying the application of a logistic regression are illustrated, and the types of questions to which such a technique has been applied in the stuttering field are outlined. The assumptions and limitations of the technique are discussed with respect to existing stuttering research, and with respect to formulating appropriate research strategies to accommodate these considerations. Finally, some alternatives to the approach are briefly discussed. The way the statistical procedures are employed are demonstrated with some hypothetical data. Research into several practical issues concerning stuttering could benefit if risk factor modelling were used. Important examples are early diagnosis, prognosis (whether a child will recover or persist) and assessment of treatment outcome. After reading this article you will: (a) Summarize the situations in which logistic regression can be applied to a range of issues about stuttering; (b) Follow the steps in performing a logistic regression analysis; (c) Describe the assumptions of the logistic regression technique and the precautions that need to be checked when it is employed; (d) Be able to summarize its advantages over other techniques like estimation of group differences and simple regression. Copyright © 2012 Elsevier Inc. All rights reserved.

Efficient estimation of an additive quantile regression model

NARCIS (Netherlands)

Cheng, Y.; de Gooijer, J.G.; Zerom, D.

2011-01-01

In this paper, two non-parametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a more viable alternative to existing kernel-based approaches. The second estimator

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

Science.gov (United States)

Marill, Keith A

2004-01-01

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

Advanced statistics: linear regression, part II: multiple linear regression.

Science.gov (United States)

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

Dynamics of the Seychelles-Chagos Thermocline Ridge

Science.gov (United States)

Bulusu, S.

2016-02-01

multilinear regression model (MRM). This study will help to improve monsoon modeling and forecasting, two areas that remain highly inaccurate after decades of research work.

Fluvial Channel Networks as Analogs for the Ridge-Forming Unit, Sinus Meridiani, Mars

Science.gov (United States)

Wilkinson, M. J.; du Bois, J. B.

2010-01-01

Fluvial models have been generally discounted as analogs for the younger layered rock units of Sinus Meridiani. A fluvial model based on the large fluvial fan provides a possibly close analog for various features of the sinuous ridges of the etched, ridge-forming unit (RFU) in particular. The close spacing of the RFU ridges, their apparently chaotic orientations, and their organization in dense networks all appear unlike classical stream channel patterns. However, drainage patterns on large fluvial fans low-angle, fluvial aggradational features, 100s of km long, documented worldwide by us provide parallels. Some large fan characteristics resemble those of classical floodplains, but many differences have been demonstrated. One major distinction relevant to the RFU is that channel landscapes of large fans can dominate large areas (1.2 million km2 in one S. American study area). We compare channel morphologies on large fans in the southern Sahara Desert with ridge patterns in Sinus Meridiani (fig 1). Stream channels are the dominant landform on large terrestrial fans: they may equate to the ubiquitous, sinuous, elongated ridges of the RFU that cover areas region wide. Networks of convergent/divergent and crossing channels may equate to similar features in the ridge networks. Downslope divergence is absent in channels of terrestrial upland erosional landscapes (fig. 1, left), whereas it is common to both large fans (fig. 1, center) and RFU ridge patterns (fig 1, right downslope defined as the regional NW slope of Sinus Meridiani). RFU ridge orientation, judged from those areas apparently devoid of impact crater control, is broadly parallel with the regional slope (arrow, fig. 1, right), as is mean orientation of major channels on large fans (arrow, fig. 1, center). High densities per unit area characterize fan channels and martian ridges reaching an order of magnitude higher than those in uplands just upstream of the terrestrial study areas fig. 1. In concert with

Crime Modeling using Spatial Regression Approach

Science.gov (United States)

Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.

2018-01-01

Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.

Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

Science.gov (United States)

Nikbakht, Roya; Bahrampour, Abbas

2017-01-01

Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

Efficient estimation of an additive quantile regression model

NARCIS (Netherlands)

Cheng, Y.; de Gooijer, J.G.; Zerom, D.

2009-01-01

In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By

Efficient estimation of an additive quantile regression model

NARCIS (Netherlands)

Cheng, Y.; de Gooijer, J.G.; Zerom, D.

2010-01-01

In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By

Boosted beta regression.

Directory of Open Access Journals (Sweden)

Matthias Schmid

Full Text Available Regression analysis with a bounded outcome is a common problem in applied statistics. Typical examples include regression models for percentage outcomes and the analysis of ratings that are measured on a bounded scale. In this paper, we consider beta regression, which is a generalization of logit models to situations where the response is continuous on the interval (0,1. Consequently, beta regression is a convenient tool for analyzing percentage responses. The classical approach to fit a beta regression model is to use maximum likelihood estimation with subsequent AIC-based variable selection. As an alternative to this established - yet unstable - approach, we propose a new estimation technique called boosted beta regression. With boosted beta regression estimation and variable selection can be carried out simultaneously in a highly efficient way. Additionally, both the mean and the variance of a percentage response can be modeled using flexible nonlinear covariate effects. As a consequence, the new method accounts for common problems such as overdispersion and non-binomial variance structures.

Using the classical linear regression model in analysis of the dependences of conveyor belt life

Directory of Open Access Journals (Sweden)

Miriam Andrejiová

2013-12-01

Full Text Available The paper deals with the classical linear regression model of the dependence of conveyor belt life on some selected parameters: thickness of paint layer, width and length of the belt, conveyor speed and quantity of transported material. The first part of the article is about regression model design, point and interval estimation of parameters, verification of statistical significance of the model, and about the parameters of the proposed regression model. The second part of the article deals with identification of influential and extreme values that can have an impact on estimation of regression model parameters. The third part focuses on assumptions of the classical regression model, i.e. on verification of independence assumptions, normality and homoscedasticity of residuals.

Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear regression models.

Science.gov (United States)

Zhang, Xin; Liu, Pan; Chen, Yuguang; Bai, Lu; Wang, Wei

2014-01-01

The primary objective of this study was to identify whether the frequency of traffic conflicts at signalized intersections can be modeled. The opposing left-turn conflicts were selected for the development of conflict predictive models. Using data collected at 30 approaches at 20 signalized intersections, the underlying distributions of the conflicts under different traffic conditions were examined. Different conflict-predictive models were developed to relate the frequency of opposing left-turn conflicts to various explanatory variables. The models considered include a linear regression model, a negative binomial model, and separate models developed for four traffic scenarios. The prediction performance of different models was compared. The frequency of traffic conflicts follows a negative binominal distribution. The linear regression model is not appropriate for the conflict frequency data. In addition, drivers behaved differently under different traffic conditions. Accordingly, the effects of conflicting traffic volumes on conflict frequency vary across different traffic conditions. The occurrences of traffic conflicts at signalized intersections can be modeled using generalized linear regression models. The use of conflict predictive models has potential to expand the uses of surrogate safety measures in safety estimation and evaluation.

Use of empirical likelihood to calibrate auxiliary information in partly linear monotone regression models.

Science.gov (United States)

Chen, Baojiang; Qin, Jing

2014-05-10

In statistical analysis, a regression model is needed if one is interested in finding the relationship between a response variable and covariates. When the response depends on the covariate, then it may also depend on the function of this covariate. If one has no knowledge of this functional form but expect for monotonic increasing or decreasing, then the isotonic regression model is preferable. Estimation of parameters for isotonic regression models is based on the pool-adjacent-violators algorithm (PAVA), where the monotonicity constraints are built in. With missing data, people often employ the augmented estimating method to improve estimation efficiency by incorporating auxiliary information through a working regression model. However, under the framework of the isotonic regression model, the PAVA does not work as the monotonicity constraints are violated. In this paper, we develop an empirical likelihood-based method for isotonic regression model to incorporate the auxiliary information. Because the monotonicity constraints still hold, the PAVA can be used for parameter estimation. Simulation studies demonstrate that the proposed method can yield more efficient estimates, and in some situations, the efficiency improvement is substantial. We apply this method to a dementia study. Copyright © 2013 John Wiley & Sons, Ltd.

Predictive market segmentation model: An application of logistic regression model and CHAID procedure

Directory of Open Access Journals (Sweden)

Soldić-Aleksić Jasna

2009-01-01

Full Text Available Market segmentation presents one of the key concepts of the modern marketing. The main goal of market segmentation is focused on creating groups (segments of customers that have similar characteristics, needs, wishes and/or similar behavior regarding the purchase of concrete product/service. Companies can create specific marketing plan for each of these segments and therefore gain short or long term competitive advantage on the market. Depending on the concrete marketing goal, different segmentation schemes and techniques may be applied. This paper presents a predictive market segmentation model based on the application of logistic regression model and CHAID analysis. The logistic regression model was used for the purpose of variables selection (from the initial pool of eleven variables which are statistically significant for explaining the dependent variable. Selected variables were afterwards included in the CHAID procedure that generated the predictive market segmentation model. The model results are presented on the concrete empirical example in the following form: summary model results, CHAID tree, Gain chart, Index chart, risk and classification tables.

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

Science.gov (United States)

Ulbrich, Norbert Manfred

2013-01-01

A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.

Evaluation of accuracy of linear regression models in predicting urban stormwater discharge characteristics.

Science.gov (United States)

Madarang, Krish J; Kang, Joo-Hyon

2014-06-01

Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days (ADD), have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency's Storm Water Management Model (SWMM). The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid (TSS) discharge loads and event mean concentrations (EMC) were extracted. From these data, linear regression models were developed. R(2) and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Improving sub-pixel imperviousness change prediction by ensembling heterogeneous non-linear regression models

Science.gov (United States)

Drzewiecki, Wojciech

2016-12-01

In this work nine non-linear regression models were compared for sub-pixel impervious surface area mapping from Landsat images. The comparison was done in three study areas both for accuracy of imperviousness coverage evaluation in individual points in time and accuracy of imperviousness change assessment. The performance of individual machine learning algorithms (Cubist, Random Forest, stochastic gradient boosting of regression trees, k-nearest neighbors regression, random k-nearest neighbors regression, Multivariate Adaptive Regression Splines, averaged neural networks, and support vector machines with polynomial and radial kernels) was also compared with the performance of heterogeneous model ensembles constructed from the best models trained using particular techniques. The results proved that in case of sub-pixel evaluation the most accurate prediction of change may not necessarily be based on the most accurate individual assessments. When single methods are considered, based on obtained results Cubist algorithm may be advised for Landsat based mapping of imperviousness for single dates. However, Random Forest may be endorsed when the most reliable evaluation of imperviousness change is the primary goal. It gave lower accuracies for individual assessments, but better prediction of change due to more correlated errors of individual predictions. Heterogeneous model ensembles performed for individual time points assessments at least as well as the best individual models. In case of imperviousness change assessment the ensembles always outperformed single model approaches. It means that it is possible to improve the accuracy of sub-pixel imperviousness change assessment using ensembles of heterogeneous non-linear regression models.

The beach ridges of India: A review

Digital Repository Service at National Institute of Oceanography (India)

Kunte, P.D.; Wagle, B.G.

, and is presented in a consolidated form. Beach ridges of the east and west coast of India are grouped in thirteen-beach ridge complexes based on their association. Review indicates that the beach ridges of India are not older than the Holocene age...

Regularized multivariate regression models with skew-t error distributions

KAUST Repository

Chen, Lianfu; Pourahmadi, Mohsen; Maadooliat, Mehdi

2014-01-01

We consider regularization of the parameters in multivariate linear regression models with the errors having a multivariate skew-t distribution. An iterative penalized likelihood procedure is proposed for constructing sparse estimators of both

Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system

International Nuclear Information System (INIS)

Fang, Tingting; Lahdelma, Risto

2016-01-01

Highlights: • Social factor is considered for the linear regression models besides weather file. • Simultaneously optimize all the coefficients for linear regression models. • SARIMA combined with linear regression is used to forecast the heat demand. • The accuracy for both linear regression and time series models are evaluated. - Abstract: Forecasting heat demand is necessary for production and operation planning of district heating (DH) systems. In this study we first propose a simple regression model where the hourly outdoor temperature and wind speed forecast the heat demand. Weekly rhythm of heat consumption as a social component is added to the model to significantly improve the accuracy. The other type of model is the seasonal autoregressive integrated moving average (SARIMA) model with exogenous variables as a combination to take weather factors, and the historical heat consumption data as depending variables. One outstanding advantage of the model is that it peruses the high accuracy for both long-term and short-term forecast by considering both exogenous factors and time series. The forecasting performance of both linear regression models and time series model are evaluated based on real-life heat demand data for the city of Espoo in Finland by out-of-sample tests for the last 20 full weeks of the year. The results indicate that the proposed linear regression model (T168h) using 168-h demand pattern with midweek holidays classified as Saturdays or Sundays gives the highest accuracy and strong robustness among all the tested models based on the tested forecasting horizon and corresponding data. Considering the parsimony of the input, the ease of use and the high accuracy, the proposed T168h model is the best in practice. The heat demand forecasting model can also be developed for individual buildings if automated meter reading customer measurements are available. This would allow forecasting the heat demand based on more accurate heat consumption

Remedial Investigation Report on Chestnut Ridge Operable Unit 2 (Filled Coal Ash Pond/Upper McCoy Branch) at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee. Volume 1. Main Text

International Nuclear Information System (INIS)

1994-08-01

This document is a report on the remedial investigation (RI) of Chestnut Ridge Operable Unit (OU) 2 at the Oak Ridge Y-12 Plant. Chestnut Ridge OU 2 consists of Upper McCoy Branch (UMB), the Filled Coal Ash Pond (FCAP), and the area surrounding the Sluice Channel formerly associated with coal ash disposal in the FCAP. Chestnut Ridge OU 2 is located within the U.S. Department of Energy's (DOE's) Oak Ridge Reservation in Anderson County, Tennessee, approximately 24 miles west of Knoxville. The pond is an 8.5-acre area on the southern slope of Chestnut Ridge, 0.5 mile south of the main Y-12 Plant and geographically separated from the Y-12 Plant by Chestnut Ridge. The elevation of the FCAP is ∼ 950 ft above mean sea level (msl), and it is relatively flat and largely vegetated. Two small ponds are usually present at the northeast and northwest comers of the FCAP. The Sluice Channel Area extends ∼1000 ft from the northern margin of the FCAP to the crest of Chestnut Ridge, which has an elevation of ∼1100 ft above msl. The Sluice Channel Area is largely vegetated also. McCoy Branch runs from the top of Chestnut Ridge across the FCAP into Rogers Quarry and out of the quarry where it runs a short distance into Milton Hill Lake at McCoy Embayment, termed UMB. The portion south of Rogers Quarry, within Chestnut Ridge OU 4, is termed Lower McCoy Branch. The DOE Oak Ridge Y-12 Plant disposed of coal ash from its steam plant operations as a slurry that was discharged into an ash retention impoundment; this impoundment is the FCAP. The FCAP was built in 1955 to serve as a settling basin after coal ash slurried over Chestnut Ridge from the Y-12 Plant. The FCAP was constructed by building an earthen dam across the northern tributary of McCoy Branch. The dam was designed to hold 20 years of Y-12 steam plant ash. By July 1967, ash had filled up the impoundment storage behind the dam to within 4 ft of the top

Remedial Investigation Report on Chestnut Ridge Operable Unit 2 (Filled Coal Ash Pond/Upper McCoy Branch) at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee. Volume 1. Main Text

Energy Technology Data Exchange (ETDEWEB)

1994-08-01

This document is a report on the remedial investigation (RI) of Chestnut Ridge Operable Unit (OU) 2 at the Oak Ridge Y-12 Plant. Chestnut Ridge OU 2 consists of Upper McCoy Branch (UMB), the Filled Coal Ash Pond (FCAP), and the area surrounding the Sluice Channel formerly associated with coal ash disposal in the FCAP. Chestnut Ridge OU 2 is located within the U.S. Department of Energy`s (DOE`s) Oak Ridge Reservation in Anderson County, Tennessee, approximately 24 miles west of Knoxville. The pond is an 8.5-acre area on the southern slope of Chestnut Ridge, 0.5 mile south of the main Y-12 Plant and geographically separated from the Y-12 Plant by Chestnut Ridge. The elevation of the FCAP is {approximately} 950 ft above mean sea level (msl), and it is relatively flat and largely vegetated. Two small ponds are usually present at the northeast and northwest comers of the FCAP. The Sluice Channel Area extends {approximately}1000 ft from the northern margin of the FCAP to the crest of Chestnut Ridge, which has an elevation of {approximately}1100 ft above msl. The Sluice Channel Area is largely vegetated also. McCoy Branch runs from the top of Chestnut Ridge across the FCAP into Rogers Quarry and out of the quarry where it runs a short distance into Milton Hill Lake at McCoy Embayment, termed UMB. The portion south of Rogers Quarry, within Chestnut Ridge OU 4, is termed Lower McCoy Branch. The DOE Oak Ridge Y-12 Plant disposed of coal ash from its steam plant operations as a slurry that was discharged into an ash retention impoundment; this impoundment is the FCAP. The FCAP was built in 1955 to serve as a settling basin after coal ash slurried over Chestnut Ridge from the Y-12 Plant. The FCAP was constructed by building an earthen dam across the northern tributary of McCoy Branch. The dam was designed to hold 20 years of Y-12 steam plant ash. By July 1967, ash had filled up the impoundment storage behind the dam to within 4 ft of the top.

Preliminary Analysis of the Knipovich Ridge Segmentation - Influence of Focused Magmatism and Ridge Obliquity on an Ultraslow Spreading System

Science.gov (United States)

Okino, K.; Curewitz, D.; Asada, M.; Tamaki, K.

2002-12-01

Bathymetry, gravity and deep-tow sonar image data are used to define the segmentation of a 400 km long portion of the ultraslow-spreading Knipovich Ridge in the Norwegian-Greenland Sea, Northeast Atlantic Ocean. Discrete volcanic centers marked by large volcanic constructions and accompanying short wavelength mantle Bouguer anomaly (MBA) lows generally resemble those of the Gakkel Ridge and the easternmost Southwest Indian Ridge (SWIR). These magmatically robust segment centers are regularly spaced about 85-100 km apart along the ridge, and are characterized by accumulated hummocky terrain, high relief, off-axis seamount chains and significant MBA lows. We suggest that these eruptive centers correspond to areas of enhanced magma flux, and that their spacing reflects the geometry of underlying mantle upwelling cells. The large-scale thermal structure of the mantle primarily controls discrete and focused magmatism, and the relatively wide spacing of these segments may reflect cool mantle beneath the ridge. Segment centers along the southern Knipovich Ridge are characterized by lower relief and smaller MBA anomalies than along the northern section of the ridge. This suggests that ridge obliquity is a secondary control on ridge construction on the Knipovich Ridge, as the obliquity changes from 35° to 49° from north to south, respectively, while spreading rate and axial depth remain approximately constant. The increased obliquity may contribute to decreased effective spreading rates, lower upwelling magma velocity and melt formation, and limited horizontal dike propagation near the surface. We also identify small, magmatically weaker segments with low relief, little or no MBA anomaly, and no off axis expression. We suggest that these segments are either fed by lateral melt migration from adjacent magmatically stronger segments or represent smaller, discrete mantle upwelling centers with short-lived melt supply.

Preliminary analysis of the Knipovich Ridge segmentation: influence of focused magmatism and ridge obliquity on an ultraslow spreading system

Science.gov (United States)

Okino, Kyoko; Curewitz, Daniel; Asada, Miho; Tamaki, Kensaku; Vogt, Peter; Crane, Kathleen

2002-09-01

Bathymetry, gravity and deep-tow sonar image data are used to define the segmentation of a 400 km long portion of the ultraslow-spreading Knipovich Ridge in the Norwegian-Greenland Sea, Northeast Atlantic Ocean. Discrete volcanic centers marked by large volcanic constructions and accompanying short wavelength mantle Bouguer anomaly (MBA) lows generally resemble those of the Gakkel Ridge and the easternmost Southwest Indian Ridge. These magmatically robust segment centers are regularly spaced about 85-100 km apart along the ridge, and are characterized by accumulated hummocky terrain, high relief, off-axis seamount chains and significant MBA lows. We suggest that these eruptive centers correspond to areas of enhanced magma flux, and that their spacing reflects the geometry of underlying mantle upwelling cells. The large-scale thermal structure of the mantle primarily controls discrete and focused magmatism, and the relatively wide spacing of these segments may reflect cool mantle beneath the ridge. Segment centers along the southern Knipovich Ridge are characterized by lower relief and smaller MBA anomalies than along the northern section of the ridge. This suggests that ridge obliquity is a secondary control on ridge construction on the Knipovich Ridge, as the obliquity changes from 35° to 49° from north to south, respectively, while spreading rate and axial depth remain approximately constant. The increased obliquity may contribute to decreased effective spreading rates, lower upwelling magma velocity and melt formation, and limited horizontal dike propagation near the surface. We also identify small, magmatically weaker segments with low relief, little or no MBA anomaly, and no off-axis expression. We suggest that these segments are either fed by lateral melt migration from adjacent magmatically stronger segments or represent smaller, discrete mantle upwelling centers with short-lived melt supply.

Comprehensive integrated planning: A process for the Oak Ridge Reservation, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1998-05-01

The Oak Ridge Comprehensive Integrated Plan is intended to assist the US Department of Energy (DOE) and contractor personnel in implementing a comprehensive integrated planning process consistent with DOE Order 430.1, Life Cycle Asset Management and Oak Ridge Operations Order 430. DOE contractors are charged with developing and producing the Comprehensive Integrated Plan, which serves as a summary document, providing information from other planning efforts regarding vision statements, missions, contextual conditions, resources and facilities, decision processes, and stakeholder involvement. The Comprehensive Integrated Plan is a planning reference that identifies primary issues regarding major changes in land and facility use and serves all programs and functions on-site as well as the Oak Ridge Operations Office and DOE Headquarters. The Oak Ridge Reservation is a valuable national resource and is managed on the basis of the principles of ecosystem management and sustainable development and how mission, economic, ecological, social, and cultural factors are used to guide land- and facility-use decisions. The long-term goals of the comprehensive integrated planning process, in priority order, are to support DOE critical missions and to stimulate the economy while maintaining a quality environment

Comprehensive integrated planning: A process for the Oak Ridge Reservation, Oak Ridge, Tennessee

Energy Technology Data Exchange (ETDEWEB)

NONE

1998-05-01

The Oak Ridge Comprehensive Integrated Plan is intended to assist the US Department of Energy (DOE) and contractor personnel in implementing a comprehensive integrated planning process consistent with DOE Order 430.1, Life Cycle Asset Management and Oak Ridge Operations Order 430. DOE contractors are charged with developing and producing the Comprehensive Integrated Plan, which serves as a summary document, providing information from other planning efforts regarding vision statements, missions, contextual conditions, resources and facilities, decision processes, and stakeholder involvement. The Comprehensive Integrated Plan is a planning reference that identifies primary issues regarding major changes in land and facility use and serves all programs and functions on-site as well as the Oak Ridge Operations Office and DOE Headquarters. The Oak Ridge Reservation is a valuable national resource and is managed on the basis of the principles of ecosystem management and sustainable development and how mission, economic, ecological, social, and cultural factors are used to guide land- and facility-use decisions. The long-term goals of the comprehensive integrated planning process, in priority order, are to support DOE critical missions and to stimulate the economy while maintaining a quality environment.

Validation of regression models for nitrate concentrations in the upper groundwater in sandy soils

International Nuclear Information System (INIS)

Sonneveld, M.P.W.; Brus, D.J.; Roelsma, J.

2010-01-01

For Dutch sandy regions, linear regression models have been developed that predict nitrate concentrations in the upper groundwater on the basis of residual nitrate contents in the soil in autumn. The objective of our study was to validate these regression models for one particular sandy region dominated by dairy farming. No data from this area were used for calibrating the regression models. The model was validated by additional probability sampling. This sample was used to estimate errors in 1) the predicted areal fractions where the EU standard of 50 mg l -1 is exceeded for farms with low N surpluses (ALT) and farms with higher N surpluses (REF); 2) predicted cumulative frequency distributions of nitrate concentration for both groups of farms. Both the errors in the predicted areal fractions as well as the errors in the predicted cumulative frequency distributions indicate that the regression models are invalid for the sandy soils of this study area. - This study indicates that linear regression models that predict nitrate concentrations in the upper groundwater using residual soil N contents should be applied with care.

Bivariate least squares linear regression: Towards a unified analytic formalism. I. Functional models

Science.gov (United States)

Caimmi, R.

2011-08-01

Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts ( York, 1966, 1969) is reviewed using a new formalism in terms of deviation (matrix) traces which, for unweighted data, reduce to usual quantities leaving aside an unessential (but dimensional) multiplicative factor. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. The classes of linear models considered are regression lines in the general case of correlated errors in X and in Y for weighted data, and in the opposite limiting situations of (i) uncorrelated errors in X and in Y, and (ii) completely correlated errors in X and in Y. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases, namely: (Y) errors in X negligible (ideally null) with respect to errors in Y; (X) errors in Y negligible (ideally null) with respect to errors in X; (O) genuine orthogonal regression; (R) reduced major-axis regression. In the limit of unweighted data, the results determined for functional models are compared with their counterparts related to extreme structural models i.e. the instrumental scatter is negligible (ideally null) with respect to the intrinsic scatter ( Isobe et al., 1990; Feigelson and Babu, 1992). While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with the exception of Y models. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors (∓ σ) for both

Modeling and prediction of flotation performance using support vector regression

Directory of Open Access Journals (Sweden)

Despotović Vladimir

2017-01-01

Full Text Available Continuous efforts have been made in recent year to improve the process of paper recycling, as it is of critical importance for saving the wood, water and energy resources. Flotation deinking is considered to be one of the key methods for separation of ink particles from the cellulose fibres. Attempts to model the flotation deinking process have often resulted in complex models that are difficult to implement and use. In this paper a model for prediction of flotation performance based on Support Vector Regression (SVR, is presented. Representative data samples were created in laboratory, under a variety of practical control variables for the flotation deinking process, including different reagents, pH values and flotation residence time. Predictive model was created that was trained on these data samples, and the flotation performance was assessed showing that Support Vector Regression is a promising method even when dataset used for training the model is limited.

Effective porosity and density of carbonate rocks (Maynardville Limestone and Copper Ridge Dolomite) within Bear Creek Valley on the Oak Ridge Reservation based on modern petrophysical techniques

International Nuclear Information System (INIS)

Dorsch, J.

1997-02-01

The purpose of this study is to provide quantitative data on effective porosity of carbonate rock from the Maynardville Limestone and Copper Ridge Dolomite within Bear Creek Valley based on modern petrophysical techniques. The data will be useful for groundwater-flow and contaminant-flow modeling in the vicinity of the Y-12 Plant on the Oak Ridge Reservation (ORR). Furthermore, the data provides needed information on the amount of interconnected pore space potentially available for operation of matrix diffusion as a transport process within the fractured carbonate rock. A second aspect of this study is to compare effective porosity data based on modern petrophysical techniques to effective porosity data determined earlier by Goldstrand et al. (1995) with a different technique. An added bonus of the study is quantitative data on the bulk density and grain density of dolostone and limestone of the Maynardville Limestone and Copper Ridge Dolomite which might find use for geophysical modeling on the ORR

Forecasting daily meteorological time series using ARIMA and regression models

Science.gov (United States)

Murat, Małgorzata; Malinowska, Iwona; Gos, Magdalena; Krzyszczak, Jaromir

2018-04-01

The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt- Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts.

Replica analysis of overfitting in regression models for time-to-event data

Science.gov (United States)

Coolen, A. C. C.; Barrett, J. E.; Paga, P.; Perez-Vicente, C. J.

2017-09-01

Overfitting, which happens when the number of parameters in a model is too large compared to the number of data points available for determining these parameters, is a serious and growing problem in survival analysis. While modern medicine presents us with data of unprecedented dimensionality, these data cannot yet be used effectively for clinical outcome prediction. Standard error measures in maximum likelihood regression, such as p-values and z-scores, are blind to overfitting, and even for Cox’s proportional hazards model (the main tool of medical statisticians), one finds in literature only rules of thumb on the number of samples required to avoid overfitting. In this paper we present a mathematical theory of overfitting in regression models for time-to-event data, which aims to increase our quantitative understanding of the problem and provide practical tools with which to correct regression outcomes for the impact of overfitting. It is based on the replica method, a statistical mechanical technique for the analysis of heterogeneous many-variable systems that has been used successfully for several decades in physics, biology, and computer science, but not yet in medical statistics. We develop the theory initially for arbitrary regression models for time-to-event data, and verify its predictions in detail for the popular Cox model.

Cross-validation pitfalls when selecting and assessing regression and classification models.

Science.gov (United States)

Krstajic, Damjan; Buturovic, Ljubomir J; Leahy, David E; Thomas, Simon

2014-03-29

We address the problem of selecting and assessing classification and regression models using cross-validation. Current state-of-the-art methods can yield models with high variance, rendering them unsuitable for a number of practical applications including QSAR. In this paper we describe and evaluate best practices which improve reliability and increase confidence in selected models. A key operational component of the proposed methods is cloud computing which enables routine use of previously infeasible approaches. We describe in detail an algorithm for repeated grid-search V-fold cross-validation for parameter tuning in classification and regression, and we define a repeated nested cross-validation algorithm for model assessment. As regards variable selection and parameter tuning we define two algorithms (repeated grid-search cross-validation and double cross-validation), and provide arguments for using the repeated grid-search in the general case. We show results of our algorithms on seven QSAR datasets. The variation of the prediction performance, which is the result of choosing different splits of the dataset in V-fold cross-validation, needs to be taken into account when selecting and assessing classification and regression models. We demonstrate the importance of repeating cross-validation when selecting an optimal model, as well as the importance of repeating nested cross-validation when assessing a prediction error.

Geospatializing The Klang Gate Quartz Ridge in Malaysia: A Technological Perspective

Science.gov (United States)

Azahari Razak, Khamarrul; Mohamad, Zakaria; Zaki Ibrahim, Mohd; Azad Rosle, Qalam; Hattanajmie Abd Wahab, Mohd; Abu Bakar, Rabieahtul; Mohd Akib, Wan Abdul Aziz Wan

2015-04-01

Establishment of inventories on geological heritage, or geoheritage resources is a step forward for a comprehensive geoheritage management leading to a better conservation at national and global levels. Compiling and updating inventory of geoheritage is a tedious process and even so in a tropical environment. Malaysia has a tremendous list of geodiversity and generating its national database is a multi-institutional effort and worthwhile investment. However, producing accurate and reliable characteristics of such landform and spectacular geological features remained elusive. The advanced and modern mapping techniques have revolutionized the mapping, monitoring and modelling of the earth surface processes and landforms. Yet the methods for quantification of geodiversity physical features are not fully utilized in Malaysia for a better understanding its processes and activity. This study provides a better insight into the use of advanced active remote sensing technology for characterizing the forested Quartz Ridge in Malaysia. We have developed the novel method and tested in the Klang Gates Quartz Ridge, Selangor. The granitic country rock made up by quartz mineral is known as the longest quartz ridge in Malaysia and characterized by rugged topography, steep slopes, densely vegetated terrain and also rich-biodiversity area. This study presents an integrated field methodological framework and processing scheme by taking into account the climatic, topographic, geologic, and anthropogenic challenges in an equatorial region. Advanced terrestrial laser scanning system was used to accurately capture, map and model the ridge carried out within a relatively stringent time period. The high frequency Global Navigation Satellite System and modern Total Station coupled with the optical satellite and radar imageries and also advanced spatial analysis were fully utilized in the field campaign and data assessment performed during the recent monsoon season. As a result, the mapping

A LATENT CLASS POISSON REGRESSION-MODEL FOR HETEROGENEOUS COUNT DATA

NARCIS (Netherlands)

WEDEL, M; DESARBO, WS; BULT, [No Value; RAMASWAMY, [No Value

1993-01-01

In this paper an approach is developed that accommodates heterogeneity in Poisson regression models for count data. The model developed assumes that heterogeneity arises from a distribution of both the intercept and the coefficients of the explanatory variables. We assume that the mixing

Predicting Error Bars for QSAR Models

International Nuclear Information System (INIS)

Schroeter, Timon; Schwaighofer, Anton; Mika, Sebastian; Ter Laak, Antonius; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Mueller, Klaus-Robert

2007-01-01

Unfavorable physicochemical properties often cause drug failures. It is therefore important to take lipophilicity and water solubility into account early on in lead discovery. This study presents log D 7 models built using Gaussian Process regression, Support Vector Machines, decision trees and ridge regression algorithms based on 14556 drug discovery compounds of Bayer Schering Pharma. A blind test was conducted using 7013 new measurements from the last months. We also present independent evaluations using public data. Apart from accuracy, we discuss the quality of error bars that can be computed by Gaussian Process models, and ensemble and distance based techniques for the other modelling approaches

Oak Ridge Leadership Computing Facility (OLCF)

Data.gov (United States)

Federal Laboratory Consortium — The Oak Ridge Leadership Computing Facility (OLCF) was established at Oak Ridge National Laboratory in 2004 with the mission of standing up a supercomputer 100 times...

Continuous validation of ASTEC containment models and regression testing

International Nuclear Information System (INIS)

Nowack, Holger; Reinke, Nils; Sonnenkalb, Martin

2014-01-01

The focus of the ASTEC (Accident Source Term Evaluation Code) development at GRS is primarily on the containment module CPA (Containment Part of ASTEC), whose modelling is to a large extent based on the GRS containment code COCOSYS (COntainment COde SYStem). Validation is usually understood as the approval of the modelling capabilities by calculations of appropriate experiments done by external users different from the code developers. During the development process of ASTEC CPA, bugs and unintended side effects may occur, which leads to changes in the results of the initially conducted validation. Due to the involvement of a considerable number of developers in the coding of ASTEC modules, validation of the code alone, even if executed repeatedly, is not sufficient. Therefore, a regression testing procedure has been implemented in order to ensure that the initially obtained validation results are still valid with succeeding code versions. Within the regression testing procedure, calculations of experiments and plant sequences are performed with the same input deck but applying two different code versions. For every test-case the up-to-date code version is compared to the preceding one on the basis of physical parameters deemed to be characteristic for the test-case under consideration. In the case of post-calculations of experiments also a comparison to experimental data is carried out. Three validation cases from the regression testing procedure are presented within this paper. The very good post-calculation of the HDR E11.1 experiment shows the high quality modelling of thermal-hydraulics in ASTEC CPA. Aerosol behaviour is validated on the BMC VANAM M3 experiment, and the results show also a very good agreement with experimental data. Finally, iodine behaviour is checked in the validation test-case of the THAI IOD-11 experiment. Within this test-case, the comparison of the ASTEC versions V2.0r1 and V2.0r2 shows how an error was detected by the regression testing

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

Science.gov (United States)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

US Department of Energy Oak Ridge Operations Environmental Management Public Involvement Plan for the Oak Ridge Reservation

International Nuclear Information System (INIS)

1996-03-01

This document was prepared in accordance with CERCLA requirements for writing community relations plans. It includes information on how the DOE Oak Ridge Operations Office prepares and executes Environmental Management Community relations activities. It is divided into three sections: the public involvement plan, public involvement in Oak Ridge, and public involvement in 1995. Four appendices are also included: environmental management in Oak Ridge; community and regional overview; key laws, agreements, and policy; and principal contacts

Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling.

Science.gov (United States)

Kawashima, Issaku; Kumano, Hiroaki

2017-01-01

Mind-wandering (MW), task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG) variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR) to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

Prediction of Mind-Wandering with Electroencephalogram and Non-linear Regression Modeling

Directory of Open Access Journals (Sweden)

Issaku Kawashima

2017-07-01

Full Text Available Mind-wandering (MW, task-unrelated thought, has been examined by researchers in an increasing number of articles using models to predict whether subjects are in MW, using numerous physiological variables. However, these models are not applicable in general situations. Moreover, they output only binary classification. The current study suggests that the combination of electroencephalogram (EEG variables and non-linear regression modeling can be a good indicator of MW intensity. We recorded EEGs of 50 subjects during the performance of a Sustained Attention to Response Task, including a thought sampling probe that inquired the focus of attention. We calculated the power and coherence value and prepared 35 patterns of variable combinations and applied Support Vector machine Regression (SVR to them. Finally, we chose four SVR models: two of them non-linear models and the others linear models; two of the four models are composed of a limited number of electrodes to satisfy model usefulness. Examination using the held-out data indicated that all models had robust predictive precision and provided significantly better estimations than a linear regression model using single electrode EEG variables. Furthermore, in limited electrode condition, non-linear SVR model showed significantly better precision than linear SVR model. The method proposed in this study helps investigations into MW in various little-examined situations. Further, by measuring MW with a high temporal resolution EEG, unclear aspects of MW, such as time series variation, are expected to be revealed. Furthermore, our suggestion that a few electrodes can also predict MW contributes to the development of neuro-feedback studies.

Reconstruction of missing daily streamflow data using dynamic regression models

Science.gov (United States)

Tencaliec, Patricia; Favre, Anne-Catherine; Prieur, Clémentine; Mathevet, Thibault

2015-12-01

River discharge is one of the most important quantities in hydrology. It provides fundamental records for water resources management and climate change monitoring. Even very short data-gaps in this information can cause extremely different analysis outputs. Therefore, reconstructing missing data of incomplete data sets is an important step regarding the performance of the environmental models, engineering, and research applications, thus it presents a great challenge. The objective of this paper is to introduce an effective technique for reconstructing missing daily discharge data when one has access to only daily streamflow data. The proposed procedure uses a combination of regression and autoregressive integrated moving average models (ARIMA) called dynamic regression model. This model uses the linear relationship between neighbor and correlated stations and then adjusts the residual term by fitting an ARIMA structure. Application of the model to eight daily streamflow data for the Durance river watershed showed that the model yields reliable estimates for the missing data in the time series. Simulation studies were also conducted to evaluate the performance of the procedure.

InRidge program: Preliminary results from the first cruise

Digital Repository Service at National Institute of Oceanography (India)

Mukhopadhyay, R.; Murthy, K.S.R.; Iyer, S.D.; Rao, M.M.M.; Banerjee, R.; Subrahmanyam, A.S.; Shirodkar, P.V.; Ghose, I.

The first cruise under India's own Ridge research initiative, InRidge collected new data on bathymetry, free-air gravity and magnetic anomalies across the ridge axis between the Vema and Zhivago transform faults in the Central Indian Ridge...

Detection of Outliers in Regression Model for Medical Data

Directory of Open Access Journals (Sweden)

Stephen Raj S

2017-07-01

Full Text Available In regression analysis, an outlier is an observation for which the residual is large in magnitude compared to other observations in the data set. The detection of outliers and influential points is an important step of the regression analysis. Outlier detection methods have been used to detect and remove anomalous values from data. In this paper, we detect the presence of outliers in simple linear regression models for medical data set. Chatterjee and Hadi mentioned that the ordinary residuals are not appropriate for diagnostic purposes; a transformed version of them is preferable. First, we investigate the presence of outliers based on existing procedures of residuals and standardized residuals. Next, we have used the new approach of standardized scores for detecting outliers without the use of predicted values. The performance of the new approach was verified with the real-life data.

Modelos ridge em planejamentos de misturas: uma aplicação na extração da polpa de pequi Ridge models in mixture planning: an application in the extraction of pequi pulp

Directory of Open Access Journals (Sweden)

Tania Miranda Nepomucena

2013-01-01

Full Text Available Mixture Models can be used in experimental situations involving areas related to food science and chemistry. Some problems of a statistical nature can be found, such as effects of multicollinearity that result in uncertainty in the optimization of a dependent variable. This study proposes the application of the ridge model adapted for mixture planning considering the Kronecker (K-model and Scheffe (S-Model methods applied to response surfaces. The method determined the proportions of hexane, acetone and alcohol proportions that resulted in the maximum response of percentage of extracted pequi (Caryocar brasiliense pulp oil.

Estimasi Model Seemingly Unrelated Regression (SUR dengan Metode Generalized Least Square (GLS

Directory of Open Access Journals (Sweden)

Ade Widyaningsih

2015-04-01

Full Text Available Regression analysis is a statistical tool that is used to determine the relationship between two or more quantitative variables so that one variable can be predicted from the other variables. A method that can used to obtain a good estimation in the regression analysis is ordinary least squares method. The least squares method is used to estimate the parameters of one or more regression but relationships among the errors in the response of other estimators are not allowed. One way to overcome this problem is Seemingly Unrelated Regression model (SUR in which parameters are estimated using Generalized Least Square (GLS. In this study, the author applies SUR model using GLS method on world gasoline demand data. The author obtains that SUR using GLS is better than OLS because SUR produce smaller errors than the OLS.

Estimasi Model Seemingly Unrelated Regression (SUR dengan Metode Generalized Least Square (GLS

Directory of Open Access Journals (Sweden)

Ade Widyaningsih

2014-06-01

Full Text Available Regression analysis is a statistical tool that is used to determine the relationship between two or more quantitative variables so that one variable can be predicted from the other variables. A method that can used to obtain a good estimation in the regression analysis is ordinary least squares method. The least squares method is used to estimate the parameters of one or more regression but relationships among the errors in the response of other estimators are not allowed. One way to overcome this problem is Seemingly Unrelated Regression model (SUR in which parameters are estimated using Generalized Least Square (GLS. In this study, the author applies SUR model using GLS method on world gasoline demand data. The author obtains that SUR using GLS is better than OLS because SUR produce smaller errors than the OLS.

Quality assurance project plan for the Chestnut Ridge Fly Ash Pond Stabilization Project at the Oak Ridge Y-12 Plant, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

1996-07-01

The Chestnut Ridge Fly Ash Pond Stabilization (CRFAPS) Project will stabilize a 19-m-high (62-ft-high) earthen embankment across Upper McCoy Branch situated along the southern slope of Chestnut Ridge. This task will be accomplished by raising the crest of the embankment, reinforcing the face of the embankment, removing trees from the face and top of the embankment, and repairing the emergency spillway. The primary responsibilities of the team members are: Lockheed Martin Energy Systems, Inc., (Energy Systems) will be responsible for project integration, technical support, Title 3 field support, environmental oversight, and quality assurance (QA) oversight of the project; Foster Wheeler Environmental Corporation (FWENC) will be responsible for design and home office Title 3 support; MK-Ferguson of Oak Ridge Company (MK-F) will be responsible for health and safety, construction, and procurement of construction materials. Each of the team members has a QA program approved by the US Department of Energy (DOE) Oak Ridge Operations. This project-specific QA project plan (QAPP), which is applicable to all project activities, identifies and integrates the specific QA requirements from the participant's QA programs that are necessary for this project

On pseudo-values for regression analysis in competing risks models

DEFF Research Database (Denmark)

Graw, F; Gerds, Thomas Alexander; Schumacher, M

2009-01-01

For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15-27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their...

Modeling and prediction of Turkey's electricity consumption using Support Vector Regression

International Nuclear Information System (INIS)

Kavaklioglu, Kadir

2011-01-01

Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, ε-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values; and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best ε-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption. (author)

Cluster regression model and level fluctuation features of Van Lake, Turkey

Directory of Open Access Journals (Sweden)

Z. Şen

1999-02-01

Full Text Available Lake water levels change under the influences of natural and/or anthropogenic environmental conditions. Among these influences are the climate change, greenhouse effects and ozone layer depletions which are reflected in the hydrological cycle features over the lake drainage basins. Lake levels are among the most significant hydrological variables that are influenced by different atmospheric and environmental conditions. Consequently, lake level time series in many parts of the world include nonstationarity components such as shifts in the mean value, apparent or hidden periodicities. On the other hand, many lake level modeling techniques have a stationarity assumption. The main purpose of this work is to develop a cluster regression model for dealing with nonstationarity especially in the form of shifting means. The basis of this model is the combination of transition probability and classical regression technique. Both parts of the model are applied to monthly level fluctuations of Lake Van in eastern Turkey. It is observed that the cluster regression procedure does preserve the statistical properties and the transitional probabilities that are indistinguishable from the original data.Key words. Hydrology (hydrologic budget; stochastic processes · Meteorology and atmospheric dynamics (ocean-atmosphere interactions

Cluster regression model and level fluctuation features of Van Lake, Turkey

Directory of Open Access Journals (Sweden)

Z. Şen

Full Text Available Lake water levels change under the influences of natural and/or anthropogenic environmental conditions. Among these influences are the climate change, greenhouse effects and ozone layer depletions which are reflected in the hydrological cycle features over the lake drainage basins. Lake levels are among the most significant hydrological variables that are influenced by different atmospheric and environmental conditions. Consequently, lake level time series in many parts of the world include nonstationarity components such as shifts in the mean value, apparent or hidden periodicities. On the other hand, many lake level modeling techniques have a stationarity assumption. The main purpose of this work is to develop a cluster regression model for dealing with nonstationarity especially in the form of shifting means. The basis of this model is the combination of transition probability and classical regression technique. Both parts of the model are applied to monthly level fluctuations of Lake Van in eastern Turkey. It is observed that the cluster regression procedure does preserve the statistical properties and the transitional probabilities that are indistinguishable from the original data.

Key words. Hydrology (hydrologic budget; stochastic processes · Meteorology and atmospheric dynamics (ocean-atmosphere interactions

Department of Energy Air Emissions Annual Report Oak Ridge Reservation, Oak Ridge, Tennessee 40 Code of Federal Regulations (CFR) 61, Subpart H Calendar Year 2016

Energy Technology Data Exchange (ETDEWEB)

Martin, Richard [Oak Ridge Y-12 Plant (Y-12), Oak Ridge, TN (United States)

2017-06-30

As defined in the preamble of the final rule, the entire DOE facility on the Oak Ridge Reservation (ORR) must meet the 10 mrem/yr ED standard.1 In other words, the combined ED from all radiological air emission sources from Y-12 National Security Complex (Y-12 Complex), Oak Ridge National Laboratory (ORNL), East Tennessee Technology Park (ETTP), Oak Ridge Institute for Science and Education (ORISE) and any other DOE operation on the reservation must meet the 10 mrem/yr standard. Compliance with the standard is demonstrated through emission sampling, monitoring, calculations and radiation dose modeling in accordance with approved EPA methodologies and procedures. DOE estimates the ED to many individuals or receptor points in the vicinity of ORR, but it is the dose to the maximally exposed individual (MEI) that determines compliance with the standard.

Improved model of the retardance in citric acid coated ferrofluids using stepwise regression

Science.gov (United States)

Lin, J. F.; Qiu, X. R.

2017-06-01

Citric acid (CA) coated Fe3O4 ferrofluids (FFs) have been conducted for biomedical application. The magneto-optical retardance of CA coated FFs was measured by a Stokes polarimeter. Optimization and multiple regression of retardance in FFs were executed by Taguchi method and Microsoft Excel previously, and the F value of regression model was large enough. However, the model executed by Excel was not systematic. Instead we adopted the stepwise regression to model the retardance of CA coated FFs. From the results of stepwise regression by MATLAB, the developed model had highly predictable ability owing to F of 2.55897e+7 and correlation coefficient of one. The average absolute error of predicted retardances to measured retardances was just 0.0044%. Using the genetic algorithm (GA) in MATLAB, the optimized parametric combination was determined as [4.709 0.12 39.998 70.006] corresponding to the pH of suspension, molar ratio of CA to Fe3O4, CA volume, and coating temperature. The maximum retardance was found as 31.712°, close to that obtained by evolutionary solver in Excel and a relative error of -0.013%. Above all, the stepwise regression method was successfully used to model the retardance of CA coated FFs, and the maximum global retardance was determined by the use of GA.

Production, pathways and budgets of melts in mid-ocean ridges: An enthalpy based thermo-mechanical model

Science.gov (United States)

Mandal, Nibir; Sarkar, Shamik; Baruah, Amiya; Dutta, Urmi

2018-04-01

Using an enthalpy based thermo-mechanical model we provide a theoretical evaluation of melt production beneath mid-ocean ridges (MORs), and demonstrate how the melts subsequently develop their pathways to sustain the major ridge processes. Our model employs a Darcy idealization of the two-phase (solid-melt) system, accounting enthalpy (ΔH) as a function of temperature dependent liquid fraction (ϕ). Random thermal perturbations imposed in this model set in local convection that drive melts to flow through porosity controlled pathways with a typical mushroom-like 3D structure. We present across- and along-MOR axis model profiles to show the mode of occurrence of melt-rich zones within mushy regions, connected to deeper sources by single or multiple feeders. The upwelling of melts experiences two synchronous processes: 1) solidification-accretion, and 2) eruption, retaining a large melt fraction in the framework of mantle dynamics. Using a bifurcation analysis we determine the threshold condition for melt eruption, and estimate the potential volumes of eruptible melts (∼3.7 × 106 m3/yr) and sub-crustal solidified masses (∼1-8.8 × 106 m3/yr) on an axis length of 500 km. The solidification process far dominates over the eruption process in the initial phase, but declines rapidly on a time scale (t) of 1 Myr. Consequently, the eruption rate takes over the solidification rate, but attains nearly a steady value as t > 1.5 Myr. We finally present a melt budget, where a maximum of ∼5% of the total upwelling melt volume is available for eruption, whereas ∼19% for deeper level solidification; the rest continue to participate in the sub-crustal processes.

Accounting for spatial effects in land use regression for urban air pollution modeling.

Science.gov (United States)

Bertazzon, Stefania; Johnson, Markey; Eccles, Kristin; Kaplan, Gilaad G

2015-01-01

In order to accurately assess air pollution risks, health studies require spatially resolved pollution concentrations. Land-use regression (LUR) models estimate ambient concentrations at a fine spatial scale. However, spatial effects such as spatial non-stationarity and spatial autocorrelation can reduce the accuracy of LUR estimates by increasing regression errors and uncertainty; and statistical methods for resolving these effects--e.g., spatially autoregressive (SAR) and geographically weighted regression (GWR) models--may be difficult to apply simultaneously. We used an alternate approach to address spatial non-stationarity and spatial autocorrelation in LUR models for nitrogen dioxide. Traditional models were re-specified to include a variable capturing wind speed and direction, and re-fit as GWR models. Mean R(2) values for the resulting GWR-wind models (summer: 0.86, winter: 0.73) showed a 10-20% improvement over traditional LUR models. GWR-wind models effectively addressed both spatial effects and produced meaningful predictive models. These results suggest a useful method for improving spatially explicit models. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Regression analysis understanding and building business and economic models using Excel

CERN Document Server

Wilson, J Holton

2012-01-01

The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book will teach you the essential elements of building and understanding regression models in a business/economic context in an intuitive manner. The authors take a non-theoretical treatment that is accessible even if you have a limited statistical background. It is specifically designed to teach the correct use of regression, while advising you of its limitations and teaching about common pitfalls. This book describe

The prediction of intelligence in preschool children using alternative models to regression.

Science.gov (United States)

Finch, W Holmes; Chang, Mei; Davis, Andrew S; Holden, Jocelyn E; Rothlisberg, Barbara A; McIntosh, David E

2011-12-01

Statistical prediction of an outcome variable using multiple independent variables is a common practice in the social and behavioral sciences. For example, neuropsychologists are sometimes called upon to provide predictions of preinjury cognitive functioning for individuals who have suffered a traumatic brain injury. Typically, these predictions are made using standard multiple linear regression models with several demographic variables (e.g., gender, ethnicity, education level) as predictors. Prior research has shown conflicting evidence regarding the ability of such models to provide accurate predictions of outcome variables such as full-scale intelligence (FSIQ) test scores. The present study had two goals: (1) to demonstrate the utility of a set of alternative prediction methods that have been applied extensively in the natural sciences and business but have not been frequently explored in the social sciences and (2) to develop models that can be used to predict premorbid cognitive functioning in preschool children. Predictions of Stanford-Binet 5 FSIQ scores for preschool-aged children is used to compare the performance of a multiple regression model with several of these alternative methods. Results demonstrate that classification and regression trees provided more accurate predictions of FSIQ scores than does the more traditional regression approach. Implications of these results are discussed.

A primer for biomedical scientists on how to execute model II linear regression analysis.

Science.gov (United States)

Ludbrook, John

2012-04-01

1. There are two very different ways of executing linear regression analysis. One is Model I, when the x-values are fixed by the experimenter. The other is Model II, in which the x-values are free to vary and are subject to error. 2. I have received numerous complaints from biomedical scientists that they have great difficulty in executing Model II linear regression analysis. This may explain the results of a Google Scholar search, which showed that the authors of articles in journals of physiology, pharmacology and biochemistry rarely use Model II regression analysis. 3. I repeat my previous arguments in favour of using least products linear regression analysis for Model II regressions. I review three methods for executing ordinary least products (OLP) and weighted least products (WLP) regression analysis: (i) scientific calculator and/or computer spreadsheet; (ii) specific purpose computer programs; and (iii) general purpose computer programs. 4. Using a scientific calculator and/or computer spreadsheet, it is easy to obtain correct values for OLP slope and intercept, but the corresponding 95% confidence intervals (CI) are inaccurate. 5. Using specific purpose computer programs, the freeware computer program smatr gives the correct OLP regression coefficients and obtains 95% CI by bootstrapping. In addition, smatr can be used to compare the slopes of OLP lines. 6. When using general purpose computer programs, I recommend the commercial programs systat and Statistica for those who regularly undertake linear regression analysis and I give step-by-step instructions in the Supplementary Information as to how to use loss functions. © 2011 The Author. Clinical and Experimental Pharmacology and Physiology. © 2011 Blackwell Publishing Asia Pty Ltd.

Applied logistic regression

CERN Document Server

Hosmer, David W; Sturdivant, Rodney X

2013-01-01

 A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-

Evaluation of Logistic Regression and Multivariate Adaptive Regression Spline Models for Groundwater Potential Mapping Using R and GIS

Directory of Open Access Journals (Sweden)

Soyoung Park

2017-07-01

Full Text Available This study mapped and analyzed groundwater potential using two different models, logistic regression (LR and multivariate adaptive regression splines (MARS, and compared the results. A spatial database was constructed for groundwater well data and groundwater influence factors. Groundwater well data with a high potential yield of ≥70 m3/d were extracted, and 859 locations (70% were used for model training, whereas the other 365 locations (30% were used for model validation. We analyzed 16 groundwater influence factors including altitude, slope degree, slope aspect, plan curvature, profile curvature, topographic wetness index, stream power index, sediment transport index, distance from drainage, drainage density, lithology, distance from fault, fault density, distance from lineament, lineament density, and land cover. Groundwater potential maps (GPMs were constructed using LR and MARS models and tested using a receiver operating characteristics curve. Based on this analysis, the area under the curve (AUC for the success rate curve of GPMs created using the MARS and LR models was 0.867 and 0.838, and the AUC for the prediction rate curve was 0.836 and 0.801, respectively. This implies that the MARS model is useful and effective for groundwater potential analysis in the study area.

Sample size calculation to externally validate scoring systems based on logistic regression models.

Directory of Open Access Journals (Sweden)

Antonio Palazón-Bru

Full Text Available A sample size containing at least 100 events and 100 non-events has been suggested to validate a predictive model, regardless of the model being validated and that certain factors can influence calibration of the predictive model (discrimination, parameterization and incidence. Scoring systems based on binary logistic regression models are a specific type of predictive model.The aim of this study was to develop an algorithm to determine the sample size for validating a scoring system based on a binary logistic regression model and to apply it to a case study.The algorithm was based on bootstrap samples in which the area under the ROC curve, the observed event probabilities through smooth curves, and a measure to determine the lack of calibration (estimated calibration index were calculated. To illustrate its use for interested researchers, the algorithm was applied to a scoring system, based on a binary logistic regression model, to determine mortality in intensive care units.In the case study provided, the algorithm obtained a sample size with 69 events, which is lower than the value suggested in the literature.An algorithm is provided for finding the appropriate sample size to validate scoring systems based on binary logistic regression models. This could be applied to determine the sample size in other similar cases.

Detection of Cutting Tool Wear using Statistical Analysis and Regression Model

Science.gov (United States)

Ghani, Jaharah A.; Rizal, Muhammad; Nuawi, Mohd Zaki; Haron, Che Hassan Che; Ramli, Rizauddin

2010-10-01

This study presents a new method for detecting the cutting tool wear based on the measured cutting force signals. A statistical-based method called Integrated Kurtosis-based Algorithm for Z-Filter technique, called I-kaz was used for developing a regression model and 3D graphic presentation of I-kaz 3D coefficient during machining process. The machining tests were carried out using a CNC turning machine Colchester Master Tornado T4 in dry cutting condition. A Kistler 9255B dynamometer was used to measure the cutting force signals, which were transmitted, analyzed, and displayed in the DasyLab software. Various force signals from machining operation were analyzed, and each has its own I-kaz 3D coefficient. This coefficient was examined and its relationship with flank wear lands (VB) was determined. A regression model was developed due to this relationship, and results of the regression model shows that the I-kaz 3D coefficient value decreases as tool wear increases. The result then is used for real time tool wear monitoring.

Tubular Initial Conditions and Ridge Formation

Directory of Open Access Journals (Sweden)

M. S. Borysova

2013-01-01

Full Text Available The 2D azimuth and rapidity structure of the two-particle correlations in relativistic A+A collisions is altered significantly by the presence of sharp inhomogeneities in superdense matter formed in such processes. The causality constraints enforce one to associate the long-range longitudinal correlations observed in a narrow angular interval, the so-called (soft ridge, with peculiarities of the initial conditions of collision process. This study's objective is to analyze whether multiform initial tubular structures, undergoing the subsequent hydrodynamic evolution and gradual decoupling, can form the soft ridges. Motivated by the flux-tube scenarios, the initial energy density distribution contains the different numbers of high density tube-like boost-invariant inclusions that form a bumpy structure in the transverse plane. The influence of various structures of such initial conditions in the most central A+A events on the collective evolution of matter, resulting spectra, angular particle correlations and vn-coefficients is studied in the framework of the hydrokinetic model (HKM.

An aerial radiological survey of the Oak Ridge Reservation and surrounding area, Oak Ridge, Tennessee

International Nuclear Information System (INIS)

Maurer, R.J.

1989-09-01

An aerial radiological survey of the Oak Ridge Reservation (ORR) and surrounding area in Oak Ridge, Tennessee, was conducted from September 12--29, 1989. The purpose of the survey was to measure and document the site's terrestrial radiological environment for use in effective environmental management and emergency response planning. The aerial survey was flown at an altitude of 91 meters (300 feet) along a series of parallel lines 152 meters (500 feet) apart. The survey encompassed an area of 440 square kilometers (170 square miles) as defined by the Tennessee Valley Authority Map S-16A of the entire Oak Ridge Reservation and adjacent area. The results of the aerial survey are reported as inferred exposure rates at 1 meter above ground level (AGL) in the form of a radiation contour map. Typical background exposure rates were found to vary from 5 to 14 microroentgens per hour (μR/h). The man-made radionuclides, cobalt-60, cesium-137, and protactinium-234m (a radioisotope indicative of depleted uranium), were detected at several facilities on the site. In support of the aerial survey, ground-based exposure rate and soil sample measurements were obtained at several locations within the survey boundary. In addition to the large scale aerial survey, two special flyovers were requested by the Department of Energy. The first request was to conduct a survey of a 1-mile x 2-mile area in south Knoxville, Tennessee. The area had been used previously to store contaminated scrap metals from operations at the Oak Ridge site. The second request was to fly several passes over a 5-mile length of railroad tracks leading from the Oak Ridge Y-12 Plant, north through the city of Oak Ridge. The railroad tracks had been previously used in the transport of cesium-137

Strain distribution and model for formation of eastern Umtanum Ridge anticline, south-central Washington

Energy Technology Data Exchange (ETDEWEB)

Price, E.H.

1979-10-01

Umtanum Ridge in south-central Washington is the topographic expression of a complex anticline within the Yakima Fold system in the Miocene Columbia River Basalt Group. The Yakima Fold system, which is partly contained within the Hanford Site, is an example of a layered basalt sequence folded near the surface of the earth. The Pasco Basin stratigraphic nomenclature is used in this repot. Rockwelll Hanford Operations, under contract to the US Department of Energy, is investigating the feasibility of therminal high-level nuclear waste storage in mined repositories in basalt beneath the Hanford Site. Because thereis essentially no basalt within the Site that has not been involved in some folding, any potential location for a repository will be either on the limbs or near the hinge zone of a Yakima Fold structure. Umtanum Ridge is the best exposed Yakima Fold structure in the vicinity of the Site for studying the nature and three-dimensional style of deformation of a multilayered basalt sequence. The structural geometry, distribution of strain within the Umtanum structure and deformational mechanisms of the Umtanum Ridge are discussed.

Strain distribution and model for formation of eastern Umtanum Ridge anticline, south-central Washington

International Nuclear Information System (INIS)

Price, E.H.

1979-10-01

Umtanum Ridge in south-central Washington is the topographic expression of a complex anticline within the Yakima Fold system in the Miocene Columbia River Basalt Group. The Yakima Fold system, which is partly contained within the Hanford Site, is an example of a layered basalt sequence folded near the surface of the earth. The Pasco Basin stratigraphic nomenclature is used in this repot. Rockwelll Hanford Operations, under contract to the US Department of Energy, is investigating the feasibility of therminal high-level nuclear waste storage in mined repositories in basalt beneath the Hanford Site. Because thereis essentially no basalt within the Site that has not been involved in some folding, any potential location for a repository will be either on the limbs or near the hinge zone of a Yakima Fold structure. Umtanum Ridge is the best exposed Yakima Fold structure in the vicinity of the Site for studying the nature and three-dimensional style of deformation of a multilayered basalt sequence. The structural geometry, distribution of strain within the Umtanum structure and deformational mechanisms of the Umtanum Ridge are discussed

Using the Logistic Regression model in supporting decisions of establishing marketing strategies

Directory of Open Access Journals (Sweden)

Cristinel CONSTANTIN

2015-12-01

Full Text Available This paper is about an instrumental research regarding the using of Logistic Regression model for data analysis in marketing research. The decision makers inside different organisation need relevant information to support their decisions regarding the marketing strategies. The data provided by marketing research could be computed in various ways but the multivariate data analysis models can enhance the utility of the information. Among these models we can find the Logistic Regression model, which is used for dichotomous variables. Our research is based on explanation the utility of this model and interpretation of the resulted information in order to help practitioners and researchers to use it in their future investigations

Vector regression introduced

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Mok Tik

2014-06-01

Full Text Available This study formulates regression of vector data that will enable statistical analysis of various geodetic phenomena such as, polar motion, ocean currents, typhoon/hurricane tracking, crustal deformations, and precursory earthquake signals. The observed vector variable of an event (dependent vector variable is expressed as a function of a number of hypothesized phenomena realized also as vector variables (independent vector variables and/or scalar variables that are likely to impact the dependent vector variable. The proposed representation has the unique property of solving the coefficients of independent vector variables (explanatory variables also as vectors, hence it supersedes multivariate multiple regression models, in which the unknown coefficients are scalar quantities. For the solution, complex numbers are used to rep- resent vector information, and the method of least squares is deployed to estimate the vector model parameters after transforming the complex vector regression model into a real vector regression model through isomorphism. Various operational statistics for testing the predictive significance of the estimated vector parameter coefficients are also derived. A simple numerical example demonstrates the use of the proposed vector regression analysis in modeling typhoon paths.

Multiple regression models for energy use in air-conditioned office buildings in different climates

International Nuclear Information System (INIS)

Lam, Joseph C.; Wan, Kevin K.W.; Liu Dalong; Tsang, C.L.

2010-01-01

An attempt was made to develop multiple regression models for office buildings in the five major climates in China - severe cold, cold, hot summer and cold winter, mild, and hot summer and warm winter. A total of 12 key building design variables were identified through parametric and sensitivity analysis, and considered as inputs in the regression models. The coefficient of determination R 2 varies from 0.89 in Harbin to 0.97 in Kunming, indicating that 89-97% of the variations in annual building energy use can be explained by the changes in the 12 parameters. A pseudo-random number generator based on three simple multiplicative congruential generators was employed to generate random designs for evaluation of the regression models. The difference between regression-predicted and DOE-simulated annual building energy use are largely within 10%. It is envisaged that the regression models developed can be used to estimate the likely energy savings/penalty during the initial design stage when different building schemes and design concepts are being considered.

CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model

DEFF Research Database (Denmark)

Dyrholm, Mads; Hansen, Lars Kai

2004-01-01

We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....

Oak Ridge Reservation environmental report for 1991

International Nuclear Information System (INIS)

Mucke, P.C.

1992-10-01

The Oak Ridge Reservation Environmental Report for 1991 is the 21st in a series that began in 1971. The report documents the annual results of a comprehensive program to estimate the impact of the US Department of Energy (DOE) Oak Ridge operations upon human health and the environment. The report is organized into ten sections that address various aspects of effluent monitoring, environmental surveillance, dose assessment, waste management, and quality assurance. A compliance summary gives a synopsis of the status of each facility relative to applicable state and federal regulations. Data are included for the following: Oak Ridge Y-12 Plant; Oak Ridge National Laboratory (ORNL); and Oak Ridge K-25 Site. Effluent monitoring and environmental surveillance programs are intended to serve as effective indicators of contaminant releases and ambient contaminant concentrations that have the potential to result in adverse impacts to human health and the environment

Two levels ARIMAX and regression models for forecasting time series data with calendar variation effects

Science.gov (United States)

Suhartono, Lee, Muhammad Hisyam; Prastyo, Dedy Dwi

2015-12-01

The aim of this research is to develop a calendar variation model for forecasting retail sales data with the Eid ul-Fitr effect. The proposed model is based on two methods, namely two levels ARIMAX and regression methods. Two levels ARIMAX and regression models are built by using ARIMAX for the first level and regression for the second level. Monthly men's jeans and women's trousers sales in a retail company for the period January 2002 to September 2009 are used as case study. In general, two levels of calendar variation model yields two models, namely the first model to reconstruct the sales pattern that already occurred, and the second model to forecast the effect of increasing sales due to Eid ul-Fitr that affected sales at the same and the previous months. The results show that the proposed two level calendar variation model based on ARIMAX and regression methods yields better forecast compared to the seasonal ARIMA model and Neural Networks.

Formation of ridges in a stable lithosphere in mantle convection models with a viscoplastic rheology.

Science.gov (United States)

Rozel, A; Golabek, G J; Näf, R; Tackley, P J

2015-06-28

Numerical simulations of mantle convection with a viscoplastic rheology usually display mobile, episodic or stagnant lid regimes. In this study, we report a new convective regime in which a ridge can form without destabilizing the surrounding lithosphere or forming subduction zones. Using simulations in 2-D spherical annulus geometry, we show that a depth-dependent yield stress is sufficient to reach this ridge only regime. This regime occurs when the friction coefficient is close to the critical value between mobile lid and stagnant lid regimes. Maps of convective regime as a function of the parameters friction coefficients and depth dependence of viscosity are provided for both basal heating and mixed heating situations. The ridge only regime appears for both pure basal heating and mixed heating mode. For basal heating, this regime can occur for all vertical viscosity contrasts, while for mixed heating, a highly viscous deep mantle is required.

Testing and Modeling Fuel Regression Rate in a Miniature Hybrid Burner

Directory of Open Access Journals (Sweden)

Luciano Fanton

2012-01-01

Full Text Available Ballistic characterization of an extended group of innovative HTPB-based solid fuel formulations for hybrid rocket propulsion was performed in a lab-scale burner. An optical time-resolved technique was used to assess the quasisteady regression history of single perforation, cylindrical samples. The effects of metalized additives and radiant heat transfer on the regression rate of such formulations were assessed. Under the investigated operating conditions and based on phenomenological models from the literature, analyses of the collected experimental data show an appreciable influence of the radiant heat flux from burnt gases and soot for both unloaded and loaded fuel formulations. Pure HTPB regression rate data are satisfactorily reproduced, while the impressive initial regression rates of metalized formulations require further assessment.

An enhanced structure tensor method for sea ice ridge detection from GF-3 SAR imagery

Science.gov (United States)

Zhu, T.; Li, F.; Zhang, Y.; Zhang, S.; Spreen, G.; Dierking, W.; Heygster, G.

2017-12-01

In SAR imagery, ridges or leads are shown as the curvilinear features. The proposed ridge detection method is facilitated by their curvilinear shapes. The bright curvilinear features are recognized as the ridges while the dark curvilinear features are classified as the leads. In dual-polarization HH or HV channel of C-band SAR imagery, the bright curvilinear feature may be false alarm because the frost flowers of young leads may show as bright pixels associated with changes in the surface salinity under calm surface conditions. Wind roughened leads also trigger the backscatter increasing that can be misclassified as ridges [1]. Thus the width limitation is considered in this proposed structure tensor method [2], since only shape feature based method is not enough for detecting ridges. The ridge detection algorithm is based on the hypothesis that the bright pixels are ridges with curvilinear shapes and the ridge width is less 30 meters. Benefited from GF-3 with high spatial resolution of 3 meters, we provide an enhanced structure tensor method for detecting the significant ridge. The preprocessing procedures including the calibration and incidence angle normalization are also investigated. The bright pixels will have strong response to the bandpass filtering. The ridge training samples are delineated from the SAR imagery in the Log-Gabor filters to construct structure tensor. From the tensor, the dominant orientation of the pixel representing the ridge is determined by the dominant eigenvector. For the post-processing of structure tensor, the elongated kernel is desired to enhance the ridge curvilinear shape. Since ridge presents along a certain direction, the ratio of the dominant eigenvector will be used to measure the intensity of local anisotropy. The convolution filter has been utilized in the constructed structure tensor is used to model spatial contextual information. Ridge detection results from GF-3 show the proposed method performs better compared to the

Two-step variable selection in quantile regression models

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FAN Yali

2015-06-01

Full Text Available We propose a two-step variable selection procedure for high dimensional quantile regressions, in which the dimension of the covariates, pn is much larger than the sample size n. In the first step, we perform ℓ1 penalty, and we demonstrate that the first step penalized estimator with the LASSO penalty can reduce the model from an ultra-high dimensional to a model whose size has the same order as that of the true model, and the selected model can cover the true model. The second step excludes the remained irrelevant covariates by applying the adaptive LASSO penalty to the reduced model obtained from the first step. Under some regularity conditions, we show that our procedure enjoys the model selection consistency. We conduct a simulation study and a real data analysis to evaluate the finite sample performance of the proposed approach.

Regression Models for Predicting Force Coefficients of Aerofoils

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Mohammed ABDUL AKBAR

2015-09-01

Full Text Available Renewable sources of energy are attractive and advantageous in a lot of different ways. Among the renewable energy sources, wind energy is the fastest growing type. Among wind energy converters, Vertical axis wind turbines (VAWTs have received renewed interest in the past decade due to some of the advantages they possess over their horizontal axis counterparts. VAWTs have evolved into complex 3-D shapes. A key component in predicting the output of VAWTs through analytical studies is obtaining the values of lift and drag coefficients which is a function of shape of the aerofoil, ‘angle of attack’ of wind and Reynolds’s number of flow. Sandia National Laboratories have carried out extensive experiments on aerofoils for the Reynolds number in the range of those experienced by VAWTs. The volume of experimental data thus obtained is huge. The current paper discusses three Regression analysis models developed wherein lift and drag coefficients can be found out using simple formula without having to deal with the bulk of the data. Drag coefficients and Lift coefficients were being successfully estimated by regression models with R2 values as high as 0.98.

The Relationship between Economic Growth and Money Laundering – a Linear Regression Model

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Daniel Rece

2009-09-01

Full Text Available This study provides an overview of the relationship between economic growth and money laundering modeled by a least squares function. The report analyzes statistically data collected from USA, Russia, Romania and other eleven European countries, rendering a linear regression model. The study illustrates that 23.7% of the total variance in the regressand (level of money laundering is “explained” by the linear regression model. In our opinion, this model will provide critical auxiliary judgment and decision support for anti-money laundering service systems.

Regression analysis of informative current status data with the additive hazards model.

Science.gov (United States)

Zhao, Shishun; Hu, Tao; Ma, Ling; Wang, Peijie; Sun, Jianguo

2015-04-01

This paper discusses regression analysis of current status failure time data arising from the additive hazards model in the presence of informative censoring. Many methods have been developed for regression analysis of current status data under various regression models if the censoring is noninformative, and also there exists a large literature on parametric analysis of informative current status data in the context of tumorgenicity experiments. In this paper, a semiparametric maximum likelihood estimation procedure is presented and in the method, the copula model is employed to describe the relationship between the failure time of interest and the censoring time. Furthermore, I-splines are used to approximate the nonparametric functions involved and the asymptotic consistency and normality of the proposed estimators are established. A simulation study is conducted and indicates that the proposed approach works well for practical situations. An illustrative example is also provided.

Poisson regression approach for modeling fatal injury rates amongst Malaysian workers

International Nuclear Information System (INIS)

Kamarulzaman Ibrahim; Heng Khai Theng

2005-01-01

Many safety studies are based on the analysis carried out on injury surveillance data. The injury surveillance data gathered for the analysis include information on number of employees at risk of injury in each of several strata where the strata are defined in terms of a series of important predictor variables. Further insight into the relationship between fatal injury rates and predictor variables may be obtained by the poisson regression approach. Poisson regression is widely used in analyzing count data. In this study, poisson regression is used to model the relationship between fatal injury rates and predictor variables which are year (1995-2002), gender, recording system and industry type. Data for the analysis were obtained from PERKESO and Jabatan Perangkaan Malaysia. It is found that the assumption that the data follow poisson distribution has been violated. After correction for the problem of over dispersion, the predictor variables that are found to be significant in the model are gender, system of recording, industry type, two interaction effects (interaction between recording system and industry type and between year and industry type). Introduction Regression analysis is one of the most popular

Accounting for Zero Inflation of Mussel Parasite Counts Using Discrete Regression Models

Directory of Open Access Journals (Sweden)

Emel Çankaya

2017-06-01

Full Text Available In many ecological applications, the absences of species are inevitable due to either detection faults in samples or uninhabitable conditions for their existence, resulting in high number of zero counts or abundance. Usual practice for modelling such data is regression modelling of log(abundance+1 and it is well know that resulting model is inadequate for prediction purposes. New discrete models accounting for zero abundances, namely zero-inflated regression (ZIP and ZINB, Hurdle-Poisson (HP and Hurdle-Negative Binomial (HNB amongst others are widely preferred to the classical regression models. Due to the fact that mussels are one of the economically most important aquatic products of Turkey, the purpose of this study is therefore to examine the performances of these four models in determination of the significant biotic and abiotic factors on the occurrences of Nematopsis legeri parasite harming the existence of Mediterranean mussels (Mytilus galloprovincialis L.. The data collected from the three coastal regions of Sinop city in Turkey showed more than 50% of parasite counts on the average are zero-valued and model comparisons were based on information criterion. The results showed that the probability of the occurrence of this parasite is here best formulated by ZINB or HNB models and influential factors of models were found to be correspondent with ecological differences of the regions.

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis.

Science.gov (United States)

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended.

Transpiration of glasshouse rose crops: evaluation of regression models

NARCIS (Netherlands)

Baas, R.; Rijssel, van E.

2006-01-01

Regression models of transpiration (T) based on global radiation inside the greenhouse (G), with or without energy input from heating pipes (Eh) and/or vapor pressure deficit (VPD) were parameterized. Therefore, data on T, G, temperatures from air, canopy and heating pipes, and VPD from both a

Convergent Time-Varying Regression Models for Data Streams: Tracking Concept Drift by the Recursive Parzen-Based Generalized Regression Neural Networks.

Science.gov (United States)

Duda, Piotr; Jaworski, Maciej; Rutkowski, Leszek

2018-03-01

One of the greatest challenges in data mining is related to processing and analysis of massive data streams. Contrary to traditional static data mining problems, data streams require that each element is processed only once, the amount of allocated memory is constant and the models incorporate changes of investigated streams. A vast majority of available methods have been developed for data stream classification and only a few of them attempted to solve regression problems, using various heuristic approaches. In this paper, we develop mathematically justified regression models working in a time-varying environment. More specifically, we study incremental versions of generalized regression neural networks, called IGRNNs, and we prove their tracking properties - weak (in probability) and strong (with probability one) convergence assuming various concept drift scenarios. First, we present the IGRNNs, based on the Parzen kernels, for modeling stationary systems under nonstationary noise. Next, we extend our approach to modeling time-varying systems under nonstationary noise. We present several types of concept drifts to be handled by our approach in such a way that weak and strong convergence holds under certain conditions. Finally, in the series of simulations, we compare our method with commonly used heuristic approaches, based on forgetting mechanism or sliding windows, to deal with concept drift. Finally, we apply our concept in a real life scenario solving the problem of currency exchange rates prediction.

Predicting Antitumor Activity of Peptides by Consensus of Regression Models Trained on a Small Data Sample

Directory of Open Access Journals (Sweden)

Ivanka Jerić

2011-11-01

Full Text Available Predicting antitumor activity of compounds using regression models trained on a small number of compounds with measured biological activity is an ill-posed inverse problem. Yet, it occurs very often within the academic community. To counteract, up to some extent, overfitting problems caused by a small training data, we propose to use consensus of six regression models for prediction of biological activity of virtual library of compounds. The QSAR descriptors of 22 compounds related to the opioid growth factor (OGF, Tyr-Gly-Gly-Phe-Met with known antitumor activity were used to train regression models: the feed-forward artificial neural network, the k-nearest neighbor, sparseness constrained linear regression, the linear and nonlinear (with polynomial and Gaussian kernel support vector machine. Regression models were applied on a virtual library of 429 compounds that resulted in six lists with candidate compounds ranked by predicted antitumor activity. The highly ranked candidate compounds were synthesized, characterized and tested for an antiproliferative activity. Some of prepared peptides showed more pronounced activity compared with the native OGF; however, they were less active than highly ranked compounds selected previously by the radial basis function support vector machine (RBF SVM regression model. The ill-posedness of the related inverse problem causes unstable behavior of trained regression models on test data. These results point to high complexity of prediction based on the regression models trained on a small data sample.

Resolving issues at the Department of Energy/Oak Ridge Operations Facilities

International Nuclear Information System (INIS)

Row, T.H.; Adams, W.D.

1988-01-01

Waste management, like many other issues, has experienced major milestones. In 1971, the Calvert Cliff's decision resulted in an entirely different approach to the consideration of environmental impact analysis in reactor siting. The accidents at Three Mile Island and Chernobyl have had profound effects on nuclear power plant design. The high-level waste repository program has had many similar experiences that have modified the course of events. The management of radioactive, hazardous chemical and mixed waste in all of the facilities of the Oak Ridge Operations (ORO) Office of the Department of Energy (DOE) took on an entirely different meaning in 1984. On April 13, 1984, Federal Judge Robert Taylor said that DOE should proceed 'with all deliberate speed' to bring the Y-12 plant into compliance with the Resource Conservation and Recovery Act and the Clean Water Act. This decision resulted from a suit brought by the Legal Environmental Assistance Foundation (LEAF) and grew out of a continuing revelation of mercury spills and other problems related to the Oak Ridge plants of DOE. In this same time frame, other events occurred in Oak Ridge that would set the stage for major changes, to provide the supporting environment that allowed a very different and successful approach to resolving waste management issues at the DOE/ORO Facilities. This is the origin of the Oak Ridge Model which was recently adopted as the DOE Model. The concept is to assure that all stakeholders in waste management decisions have the opportunity to be participants from the first step. A discussion of many of the elements that have contributed to the success of the Model follows

Magmatic tectonic effects of high thermal regime at the site of active ridge subduction: the Chile Triple Junction model

Science.gov (United States)

Lagabrielle, Yves; Guivel, Christèle; Maury, René C.; Bourgois, Jacques; Fourcade, Serge; Martin, Hervé

2000-11-01

High thermal gradients are expected to be found at sites of subduction of very young oceanic lithosphere and more particularly at ridge-trench-trench (RTT) triple junctions, where active oceanic spreading ridges enter a subduction zone. Active tectonics, associated with the emplacement of two main types of volcanic products, (1) MORB-type magmas, and (2) calc-alkaline acidic magmas in the forearc, also characterize these plate junction domains. In this context, MORB-type magmas are generally thought to derive from the buried active spreading center subducted at shallow depths, whereas the origin of calc-alkaline acidic magmas is more problematic. One of the best constrained examples of ridge-trench interaction is the Chile Triple Junction (CTJ) located southwest of the South American plate at 46°12'S, where the active Chile spreading center enters the subduction zone. In this area, there is a clear correlation between the emplacement of magmatic products and the migration of the triple junction along the active margin. The CTJ lava population is bimodal, with mafic to intermediate lavas (48-56% SiO 2) and acidic lavas ranging from dacites to rhyolites (66-73% SiO 2). Previous models have shown that partial melting of oceanic crust plus 10-20% of sediments, leaving an amphibole- and plagioclase-rich residue, is the only process that may account for the genesis of acidic magmas. Due to special plate geometry in the CTJ area, a given section of the margin may be successively affected by the passage of several ridge segments. We emphasize that repeated passages will lead to the development of very high thermal gradients allowing melting of rocks of oceanic origin at temperatures of 800-900°C and low pressures, corresponding to depths of 10-20 km depth only. In addition, the structure of the CTJ forearc domain is dominated by horizontal displacements and tilting of crustal blocks along a network of strike-slip faults. The occurrence of such a deformed domain implies

Modelling of hydrothermal fluid circulation in a heterogeneous medium: Application to the Rainbow Vent site (Mid-Atlantic-Ridge, 36°14N)

Science.gov (United States)

Perez, F.; Mügler, C.; Jean-Baptiste, P.; Charlou, J. L.

2012-04-01

Hydrothermal activity at the axis of mid-ocean ridges is a key driver for energy and matter transfer from the interior of the Earth to the ocean floor. At mid-ocean ridges, seawater penetrates through the permeable young crust, warms at depth and exchanges chemicals with the surrounding rocks. This hot fluid focuses and flows upwards, then is expelled from the crust at hydrothermal vent sites in the form of black or white smokers completed by diffusive emissions. We developed a new numerical tool in the Cast3M software framework to model such hydrothermal circulations. Thermodynamic properties of one-phase pure water were calculated from the IAPWS formulation. This new numerical tool was validated on several test cases of convection in closed-top and open-top boxes. Simulations of hydrothermal circulation in a homogeneous-permeability porous medium also gave results in good agreement with already published simulations. We used this new numerical tool to construct a geometric and physical model configuration of the Rainbow Vent site at 36°14'N on the Mid-Atlantic Ridge. In this presentation, several configurations will be discussed, showing that high temperatures and high mass fluxes measured at the Rainbow site cannot be modelled with hydrothermal circulation in a homogeneous-permeability porous medium. We will show that these high values require the presence of a fault or a preferential pathway right below the venting site. We will propose and discuss a 2-D one-path model that allows us to simulate both high temperatures and high mass fluxes. This modelling of the hydrothermal circulation at the Rainbow site constitutes a first but necessary step to understand the origin of high concentrations of hydrogen issued from this ultramafic-hosted vent field.

Oak Ridge Reservation environmental report for 1989

International Nuclear Information System (INIS)

Jacobs, V.A.; Wilson, A.R.

1990-10-01

This two-volume report, the Oak Ridge Reservation Environmental Report for 1989, is the nineteenth in an annual series that began in 1971. It reports the results of a comprehensive, year-round program to monitor the impact of operations at the three major US Department of Energy (DOE) production and research installations in Oak Ridge on the immediate areas' and surrounding region's groundwater and surface waters, soil, air quality, vegetation and wildlife, and through these multiple and varied pathways, the resident human population. Information is presented for the environmental monitoring Quality Assurance (QA) Program, audits and reviews, waste management activities, land special environmental studies. Data are included for the Oak Ridge Y-12 Plant, Oak Ridge National Laboratory (ORNL), and Oak Ridge Gaseous Diffusion Plant (ORGDP). Volume 1 presents narratives, summaries, and conclusions based on environmental monitoring at the three DOE installations and in the surrounding environs during calendar year (CY) 1989. Volume 1 is intended to be a ''stand-alone'' report about the Oak Ridge Reservation (ORR) for the reader who does not want an in-depth review of 1989 data. Volume 2 presents the detailed data from which these conclusions have been drawn and should be used in conjunction with Volume 1

Oak Ridge Reservation environmental report for 1989

Energy Technology Data Exchange (ETDEWEB)

Jacobs, V.A.; Wilson, A.R. (eds.)

1990-10-01

This two-volume report, the Oak Ridge Reservation Environmental Report for 1989, is the nineteenth in an annual series that began in 1971. It reports the results of a comprehensive, year-round program to monitor the impact of operations at the three major US Department of Energy (DOE) production and research installations in Oak Ridge on the immediate areas' and surrounding region's groundwater and surface waters, soil, air quality, vegetation and wildlife, and through these multiple and varied pathways, the resident human population. Information is presented for the environmental monitoring Quality Assurance (QA) Program, audits and reviews, waste management activities, land special environmental studies. Data are included for the Oak Ridge Y-12 Plant, Oak Ridge National Laboratory (ORNL), and Oak Ridge Gaseous Diffusion Plant (ORGDP). Volume 1 presents narratives, summaries, and conclusions based on environmental monitoring at the three DOE installations and in the surrounding environs during calendar year (CY) 1989. Volume 1 is intended to be a stand-alone'' report about the Oak Ridge Reservation (ORR) for the reader who does not want an in-depth review of 1989 data. Volume 2 presents the detailed data from which these conclusions have been drawn and should be used in conjunction with Volume 1.

Photovoltaic Array Condition Monitoring Based on Online Regression of Performance Model

DEFF Research Database (Denmark)

Spataru, Sergiu; Sera, Dezso; Kerekes, Tamas

2013-01-01

regression modeling, from PV array production, plane-of-array irradiance, and module temperature measurements, acquired during an initial learning phase of the system. After the model has been parameterized automatically, the condition monitoring system enters the normal operation phase, where...

Extended cox regression model: The choice of timefunction

Science.gov (United States)

Isik, Hatice; Tutkun, Nihal Ata; Karasoy, Durdu

2017-07-01

Cox regression model (CRM), which takes into account the effect of censored observations, is one the most applicative and usedmodels in survival analysis to evaluate the effects of covariates. Proportional hazard (PH), requires a constant hazard ratio over time, is the assumptionofCRM. Using extended CRM provides the test of including a time dependent covariate to assess the PH assumption or an alternative model in case of nonproportional hazards. In this study, the different types of real data sets are used to choose the time function and the differences between time functions are analyzed and discussed.

Alveolar ridge rehabilitation to increase full denture retention and stability

Directory of Open Access Journals (Sweden)

Mefina Kuntjoro

2010-12-01

Full Text Available Background: Atrophic mandibular alveolar ridge generally complicates prostetic restoration expecially full denture. Low residual alveolar ridge and basal seat can cause unstable denture, permanent ulcer, pain, neuralgia, and mastication difficulty. Pre-proshetic surgery is needed to improve denture retention and stability. Augmentation is a major surgery to increase vertical height of the atrophic mandible while vestibuloplasty is aimed to increase the denture bearing area. Purpose: The augmentation and vestibuloplasty was aimed to provide stability and retentive denture atrophic mandibular alveolar ridge. Case: A 65 years old woman patient complained about uncomfortable denture. Clinical evaluate showed flat ridge in the anterior mandible, flabby tissue and candidiasis, while residual ridge height was classified into class IV. Case management: Augmentation using autograph was conducted as the mandible vertical height is less than 15 mm. Autograph was used to achieve better bone quantity and quality. Separated alveolar ridge was conducted from left to right canine region and was elevated 0.5 mm from the previous position to get new ridge in the anterior region. The separated alveolar ridge was fixated by using T-plate and ligature wire. Three months after augmentation fixation appliances was removed vestibuloplasty was performed to increase denture bearing area that can make a stable and retentive denture. Conclusion: Augmentation and vestibuloplasty can improve flat ridge to become prominent.Latar belakang: Ridge mandibula yang atrofi pada umumnya mempersulit pembuatan restorasi prostetik terutama gigi tiruan lengkap (GTL. Residual alveolar ridge dan basal seat yang rendah menyebabkan gigi tiruan menjadi tidak stabil, menimbulkan ulser permanen, nyeri, neuralgia, dan kesulitan mengunyah. Tujuan: Augmentasi dan vestibuloplasti pada ridge mandibula yang atrofi dilakukan untuk menciptakan gigi tiruan yang stabil dan retentive. Kasus: Pasien wanita

INVESTIGATION OF E-MAIL TRAFFIC BY USING ZERO-INFLATED REGRESSION MODELS

Directory of Open Access Journals (Sweden)

Yılmaz KAYA

2012-06-01

Full Text Available Based on count data obtained with a value of zero may be greater than anticipated. These types of data sets should be used to analyze by regression methods taking into account zero values. Zero- Inflated Poisson (ZIP, Zero-Inflated negative binomial (ZINB, Poisson Hurdle (PH, negative binomial Hurdle (NBH are more common approaches in modeling more zero value possessing dependent variables than expected. In the present study, the e-mail traffic of Yüzüncü Yıl University in 2009 spring semester was investigated. ZIP and ZINB, PH and NBH regression methods were applied on the data set because more zeros counting (78.9% were found in data set than expected. ZINB and NBH regression considered zero dispersion and overdispersion were found to be more accurate results due to overdispersion and zero dispersion in sending e-mail. ZINB is determined to be best model accordingto Vuong statistics and information criteria.

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

KAUST Repository

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

2013-01-01

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

Ridge and Furrow Fields

DEFF Research Database (Denmark)

Møller, Per Grau

2016-01-01

Ridge and furrow is a specific way of ploughing which makes fields of systematic ridges and furrows like a rubbing washboard. They are part of an overall openfield system, but the focus in this paper is on the functionality of the fields. There are many indications that agro-technological reasons...... systems and the establishment of basic structures like villages (with churches) and townships and states (in northern Europe). The fields can be considered as a resilient structure lasting for 800 years, along with the same basic physical structures in society....

Model-based bootstrapping when correcting for measurement error with application to logistic regression.

Science.gov (United States)

Buonaccorsi, John P; Romeo, Giovanni; Thoresen, Magne

2018-03-01

When fitting regression models, measurement error in any of the predictors typically leads to biased coefficients and incorrect inferences. A plethora of methods have been proposed to correct for this. Obtaining standard errors and confidence intervals using the corrected estimators can be challenging and, in addition, there is concern about remaining bias in the corrected estimators. The bootstrap, which is one option to address these problems, has received limited attention in this context. It has usually been employed by simply resampling observations, which, while suitable in some situations, is not always formally justified. In addition, the simple bootstrap does not allow for estimating bias in non-linear models, including logistic regression. Model-based bootstrapping, which can potentially estimate bias in addition to being robust to the original sampling or whether the measurement error variance is constant or not, has received limited attention. However, it faces challenges that are not present in handling regression models with no measurement error. This article develops new methods for model-based bootstrapping when correcting for measurement error in logistic regression with replicate measures. The methodology is illustrated using two examples, and a series of simulations are carried out to assess and compare the simple and model-based bootstrap methods, as well as other standard methods. While not always perfect, the model-based approaches offer some distinct improvements over the other methods. © 2017, The International Biometric Society.