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Model Discrimination for Nonlinear Regression Models

Borowiak (math, U. of Akron) discusses model discrimination based upon incorrect selection probability, presents diagnostic statistics and;

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Confidence Intervals in Generalized Regression Models

generalized regression model (GRM)-of various types of regression models. It also uses a likelihood-based approach for performing statistical;

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Confidence Intervals in Generalized Regression Models

generalized regression model (GRM)-of various types of regression models. It also uses a likelihood-based approach for performing statistical;

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Regression Models as a Tool in Medical Research

results is often challenging for beginners. Regression Models as a Tool in Medical Research presents the fundamental concepts and important;

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Regression Models As A Tool In Medical Research

results is often challenging for beginners. Regression Models as a Tool in Medical Research presents the fundamental concepts and important;

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Robust Nonlinear Regression

mishandled aspects of modeling R packages for both classical and robust nonlinear regression are presented in detail in the book and on an;

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Radiation Risk Estimation

covers nonlinear measurement error models, with a particular emphasis on efficiency of regression parameter estimators. In the second part, risk;

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Introduction to Multivariate Analysis

techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification and discrimination;

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Introduction to Multivariate Analysis

techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification and discrimination;

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Adaptive Regression for Modeling Nonlinear Relationships

when they are nonlinear. Data analysts will learn how to incorporate nonlinearity in one or more predictor variables into regression models for;

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Regression Models for Categorical Dependent Variables Using Stata

Although regression models for categorical dependent variables are common, few texts explain how to interpret such models. Regression;

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Regression And Time Series Model Selection

selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including;

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Logistic Regression Using SAS

models, ROC curves, the ODDSRATIO statement (for analyzing interactions), and the EFFECTPLOT statement (for graphing nonlinear effects). Also new;

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Nonlinear Regression Analysis and Its Applications

...highly recommend[ed] ...for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models;

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Nonlinear Regression Modeling for Engineering Applications

book details methods of nonlinear regression, computational algorithms,model validation, interpretation of residuals, and useful experimental;

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Logistic Regression Using SAS

models, ROC curves, the ODDSRATIO statement (for analyzing interactions), and the EFFECTPLOT statement (for graphing nonlinear effects). Also new;

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Partially Linear Models

efficient estimation, bootstrap resampling, censored data analysis, linear measurement error models, nonlinear measurement models, nonlinear and;

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Bayesian Methods for Nonlinear Classification and Regression

, regression splines, and neural networks.* Emphasis is placed on sound implementation of nonlinear models.* Discusses medical, spatial, and economic;

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Simultaneous Inference in Regression

/wl/mybook.html Most of the book focuses on normal-error linear regression models. The author presents simultaneous confidence bands for a;

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Simultaneous Inference in Regression

/wl/mybook.html Most of the book focuses on normal-error linear regression models. The author presents simultaneous confidence bands for a;

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Handbook of Regression Methods

diverse range of topics, including censored regression, nonlinear regression, generalized linear models, and semiparametric regression;

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Data Analysis Using Regression & Multi

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied;

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Regression Models

for which generalized results are preferred? Author Richard Breen provides an introduction to regression models for such data, including;

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Data Analysis

Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and;

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Data Analysis

Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond is an integrated treatment of data analysis for the social and;

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Statistical Regression Modeling with R

This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear;

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Statistical Regression Modeling with R

This book provides a concise point of reference for the most commonly used regression methods. It begins with linear and nonlinear;

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