Negative Binomial Regression
Cambridge University Press
Edition: 2, 3/17/2011
EAN 9780521198158, ISBN10: 0521198151
Hardcover, 572 pages, 23.1 x 15.5 x 3.6 cm
Language: English
This second edition of Hilbe's Negative Binomial Regression is a substantial enhancement to the popular first edition. The only text devoted entirely to the negative binomial model and its many variations, nearly every model discussed in the literature is addressed. The theoretical and distributional background of each model is discussed, together with examples of their construction, application, interpretation and evaluation. Complete Stata and R codes are provided throughout the text, with additional code (plus SAS), derivations and data provided on the book's website. Written for the practising researcher, the text begins with an examination of risk and rate ratios, and of the estimating algorithms used to model count data. The book then gives an in-depth analysis of Poisson regression and an evaluation of the meaning and nature of overdispersion, followed by a comprehensive analysis of the negative binomial distribution and of its parameterizations into various models for evaluating count data.
Preface
1. Introduction
2. The concept of risk
3. Overview of count response models
4. Methods of estimation and assessment
5. Assessment of count models
6. Poisson regression
7. Overdispersion
8. Negative binomial regression
9. Negative binomial regression
modeling
10. Alternative variance parameterizations
11. Problems with zero counts
12. Censored and truncated count models
13. Handling endogeneity and latent class models
14. Count panel models
15. Bayesian negative binomial models
Appendix A. Constructing and interpreting interactions
Appendix B. Data sets and Stata files
References
Index.
Advance praise: 'Students, developers, and practitioners in this area will all want to have this thorough guide close at hand. The wealth of theory and extensive applications using 'real' data sets and contemporary software will provide a crucial resource for their research.' William Greene, New York University