Negative binomial regression

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 Frontmatter......Page 1 Contents......Page 5 Preface......Page 9 Introduction......Page 13 1 - Overview of count response models......Page 20 2 - Methods of estimation......Page 31 3 - Poisson regression......Page 51 4 - Overdispersion......Page 63 5 - Negative binomial regression......Page 89 6 - Negative binomial regression: modeling......Page 111 7 - Alternative variance parameterizations......Page 148 8 - Problems with zero counts......Page 172 9 - Negative binomial with censoring, truncation, and sample selection......Page 191 10 - Negative binomial panel models......Page 210 Appendix A - Negative binomial log-likelihood functions......Page 245 Appendix B - Deviance functions......Page 248 Appendix C - Stata negative binominal â•fi ML algorithm......Page 249 Appendix D - Negative binomial variance functions......Page 251 Appendix E - Data sets......Page 252 References......Page 254 Author Index......Page 259 Subject Index......Page 261 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.--jacket. 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. Joseph M. Hilbe. Includes Bibliographical References (p. 242-246) And Indexes. The book describes in detail the various types of count models used by statisticians and researchers. At 572 pages in length, nearly every model discussed in the literature of count models is addressed. For each model the book shows how the model is derived, how to construct a proper modlel for a given type of count structure, how to interpret the model, and how to evaluate it in comparison to other models. Stata and R code is used for most all models described in the 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.