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Regression for Categorical Data

Table of Contents

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.

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