Negative binomial regression
Author(s)
Bibliographic Information
Negative binomial regression
Cambridge University Press, 2007
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Note
Includes bibliographical references and index
Reprinted with corrections 2008
Description and Table of Contents
Description
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
- Preface
- Introduction
- 1. Overview of count response models
- 2. Methods of estimation
- 3. The Poisson model
- 4. Overdispersion
- 5. Negative binomial regression: basics
- 6. Negative binomial regression: modeling
- 7. Alternative variance parameterizations
- 8. Problems with zero counts
- 9. Negative binomial with censoring, truncation, and sample selection
- 10. Negative binomial panel models
- Appendix A: Negative binomial log-likelihood functions
- Appendix B: Deviance functions
- Appendix C: ML negative binomial Code
- Appendix D: Negative binomial variance functions
- Appendix E: Data sets
- References
- Author index
- Index.
by "Nielsen BookData"
