Abstract
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.
Keywords
Negative binomial distributionOverdispersionCount dataQuasi-likelihoodPoisson regressionPoisson distributionComputer scienceStatisticsBinomial (polynomial)Binomial distributionStatistical modelBinomial testEconometricsMathematics
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- Year
- 2007
- Type
- book
- Citations
- 756
- Access
- Closed
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Cite This
Joseph M. Hilbe
(2007).
Negative Binomial Regression.
Cambridge University Press eBooks
.
https://doi.org/10.1017/cbo9780511811852
Identifiers
- DOI
- 10.1017/cbo9780511811852