Regression for Categorical Data

The book treats many recent developments in flexible and high-dimensional regression not normally included in books on categorical data analysis.

Gerhard Tutz (Author)

9781107009653, Cambridge University Press

Hardback, published 21 November 2011

572 pages, 98 b/w illus. 102 tables 77 exercises
25.7 x 18.5 x 3.6 cm, 1.16 kg

"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

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.

1. Introduction
2. Binary regression: the logit model
3. Generalized linear models
4. Modeling of binary data
5. Alternative binary regression models
6. Regularization and variable selection for parametric models
7. Regression analysis of count data
8. Multinomial response models
9. Ordinal response models
10. Semi- and nonparametric generalized regression
11. Tree-based methods
12. The analysis of contingency tables: log-linear and graphical models
13. Multivariate response models
14. Random effects models
15. Prediction and classification
Appendix A. Distributions
Appendix B. Some basic tools
Appendix C. Constrained estimation
Appendix D. Kullback–Leibler distance and information-based criteria of model fit
Appendix E. Numerical integration and tools for random effects modeling.

Subject Areas: Probability & statistics [PBT]