Screen LibraryNegative binomial regression
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| Main Authors: | |
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| Published: |
Cambridge University Press,
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| Publisher Address: | Cambridge, UK New York |
| Publication Dates: | 2011. |
| Literature type: | Book |
| Language: | English |
| Edition: | 2nd ed. |
| Subjects: | |
| Carrier Form: | xviii, 553 p.: ill. ; 24 cm. |
| ISBN: |
9780521198158 (hardback) 0521198151 (hardback) |
| Index Number: | O211 |
| CLC: | O211.1 |
| Call Number: | O211.1/H641/2nd.ed. |
| Contents: |
Includes bibliographical references and index. The concept of risk -- Overview of count response models -- Methods of estimation -- Assessment of count models -- Poisson regression -- Overdispersion -- Negative binomial regression -- Negative binomial regression: modeling -- Alternative variance parameterizations -- Problems with zero counts -- Censored and truncated count models -- Handling endogeneity and latent class models -- Count panel models -- Bayesian negative binomial models -- Appendix A. Constructing and interpreting interactions terms -- Appendix B. Data sets, commands, functions. "This second edition of Hilbe's Negative Binomial Regression is a substantial enhancement to the popular first edition. The only text devoted entirely to the negative binomial model and its many variations, nearly every model discussed in the literature is addressed. The theoretical and distributional background of each model is discussed, together with examples of their construction, application, interpretation, and evaluation. Complete Stata and R code are provided throughout the text, with additional code (plus SAS), derivations, and data provided on the book's website. Written for the practicing researcher, the text begins with an examination of risk and rate ratios, and of the estimating algorithms used to model count data. The book then gives an in-depth analysis of Poisson regression and an evaluation of the meaning and nature of overdispersion, followed by a comprehensive analysis of the negative binomial distribution and of its parameterizations into various models for evaluating count data"-- |