Negative binomial regression

Bibliographic Information

Negative binomial regression

Joseph M. Hilbe

Cambridge University Press, 2007

Available at  / 15 libraries

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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.

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