Negative Binomial Regression: Modeling Overdispersed Count Data Book + PRICE WATCH * Amazon pricing is not included in price watch

Negative Binomial Regression: Modeling Overdispersed Count Data Book

For practicing researchers and statisticians who need to update their knowledge, this is the first book devoted to the negative binomial model and its many variations. Covers every model currently offered in commercial statistical software packages in detail, with numerous examples of their application and specific guidance on modeling strategy.Read More

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  • Product 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.

  • 0521857724
  • 9780521857727
  • Joseph M. Hilbe
  • 23 August 2007
  • Cambridge University Press
  • Hardcover (Book)
  • 251
  • 1
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