Negative Binomial Regression

Joseph M. Hilbe

• Publisher: Cambridge University Press
• Year: 2007
• Print ISBN: 9780521857727
• eBook ISBN: 9781316044636
• Topic: Mathematics
• Subtopic: Probability & Statistics
• Index: Mathematics

At last - a book devoted to the negative binomial model and its many variations. Every model currently offered in commercial statistical software packages is discussed in detail - how each is derived, how each resolves a distributional problem, and numerous examples of their application. Many have never before been thoroughly examined in a text on count response models: the canonical negative binomial; the NB-P model, where the negative binomial exponent is itself parameterized; and negative binomial mixed models. As the models address violations of the distributional assumptions of the basic Poisson model, identifying and handling overdispersion is a unifying theme. For practising researchers and statisticians who need to update their knowledge of Poisson and negative binomial models, the book provides a comprehensive overview of estimating methods and algorithms used to model counts, as well as specific guidelines on modeling strategy and how each model can be analyzed to access goodness-of-fit.

Table of contents

• Cover
• Half-title
• Title
• Copyright
• Contents
• Preface
• Introduction
• Overview of count response models
• Methods of estimation
• Poisson regression
• Overdispersion
• Negative binomial regression
• Negative binomial regression: modeling
• Alternative variance parameterizations
• Problems with zero counts
• Negative binomial with censoring, truncation, and sample selection
• Negative binomial panel models
• Appendix A: Negative binomial log-likelihood functions
• Appendix B: Deviance functions
• Appendix C: Stata negative binominal – ML algorithm
• Appendix D: Negative binomial variance functions
• Appendix E: Data sets
• References
• Author Index
• Subject Index