Regression for Categorical Data

By: Gerhard Tutz

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Hardcover | 21 November 2011

At a Glance

Hardcover
572 Pages

Dimensions(cm)
25.4 x 17.78 x 3.18

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This book introduces basic and advanced concepts of categorical regression with a focus on the structuring constituents of regression, including regularization techniques to structure predictors. In addition to standard methods such as the logit and probit model and extensions to multivariate settings, the author presents more recent developments in flexible and high-dimensional regression, which allow weakening of assumptions on the structuring of the predictor and yield fits that are closer to the data. A generalized linear model is used as a unifying framework whenever possible in particular parametric models that are treated within this framework. Many topics not normally included in books on categorical data analysis are treated here, such as nonparametric regression; selection of predictors by regularized estimation procedures; ternative models like the hurdle model and zero-inflated regression models for count data; and non-standard tree-based ensemble methods. The book is accompanied by an R package that contains data sets and code for all the examples.

Industry Reviews

"Regression for Categorical Data is a well-written and nicely organized book. It focuses on the regression analysis of categorical data, including both binary and count data, and introduced up-to-date developments in the field." Xia Wang, Mathematical Reviews

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Introduction
Binary regression: the logit model
Generalized linear models
Modeling of binary data
Alternative binary regression models
Regularization and variable selection for parametric models
Regression analysis of count data
Multinomial response models
Ordinal response models
Semi- and nonparametric generalized regression
Tree-based methods
The analysis of contingency tables: log-linear and graphical models
Multivariate response models
Random effects models
Prediction and classification
Distributions
Some basic tools
Constrained estimation
Kullback-Leibler distance and information-based criteria of model fit
Numerical integration and tools for random effects modeling
Table of Contents provided by Publisher. All Rights Reserved.

Regression for Categorical Data

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

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