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
''Written for practicing 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 modeling guidelines, model selection techniques, methods of interpretation, and assessment of model goodness of fit. Data sets and modeling code are provided on a companion website.''--BOOK JACKET. Read more... 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