
Negative Binomial Regression
Joseph M. Hilbe(Author)
Cambridge University Press
Book
Paperback/Softback
978-0-521-67444-7 (ISBN)
The article will not be published
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.
Reviews / Votes
'I would recommend this book to researchers and students who would like to gain an overview of the negative binomial distribution and its extensions.' Fiona McElduff, University College London 'The text is well-written, easy-to-read but once started, is difficult to put down as each chapter unfolds the intricacies of the distribution.' International Statistical Review 'Every model currently offered in commercial statistical software is discussed in detail...well written and can serve as an excellent reference book for applied statisticians who would use negative binomial regression modelling for undergraduate students or graduate students.' Yuehua Wu, Zentralblatt MATHMore details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Illustrations
Worked examples or Exercises; 20 Line drawings, unspecified
ISBN-13
978-0-521-67444-7 (9780521674447)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions

Joseph M. Hilbe
Negative Binomial Regression
Book
08/2007
Cambridge University Press
€55.71
Article exhausted; check for reprint
Additional editions

Joseph M. Hilbe
Negative Binomial Regression
Book
08/2007
Cambridge University Press
€55.71
Article exhausted; check for reprint
Person
Joseph M. Hilbe is a Solar System Ambassador with NASA's Jet Propulsion Laboratory at the California Institute of Technology, an adjunct professor of statistics at Arizona State University, and an emeritus professor at the University of Hawaii. Professor Hilbe is an elected fellow of the American Statistical Association and an elected member of the International Statistical Institute (ISI), for which he is Chair of the ISI International Astrostatistics Network. He is the author of Logistic Regression Models (Chapman and Hall/CRC, 2009), a leading text on the subject, and co-author of R for Stata Users (Springer, 2010, with R. Muenchen), Generalized Estimating Equations (Chapman and Hall/CRC, 2002, with J. Hardin) and Generalized Linear Models and Extensions (Stata Press, 2001 and 2007, also with J. Hardin).
Author
Adjunct Professor of Statistics, School of Social and Family DynamicsArizona State University
Content
Preface; Introduction; 1. Overview of count response models; 2. Methods of estimation; 3. The Poisson model; 4. Overdispersion; 5. Negative binomial regression: basics; 6. Negative binomial regression: modeling; 7. Alternative variance parameterizations; 8. Problems with zero counts; 9. Negative binomial with censoring, truncation, and sample selection; 10. Negative binomial panel models; Appendix A: Negative binomial log-likelihood functions; Appendix B: Deviance functions; Appendix C: ML negative binomial Code; Appendix D: Negative binomial variance functions; Appendix E: Data sets; References; Author index; Index.