Negative Binomial Regression Hilbe, Joseph M.

Hilbe, Joseph M.

ISBN 10: 0521857724 / ISBN 13: 9780521857727

Published by Cambridge University Press, 2007

About this Item

Light shelfwear, the book is clean and readable throughout, a decent copy.

Seller Inventory # 086553-3

Bibliographic Details

• Title: Negative Binomial Regression Hilbe, Joseph M.
• Publisher: Cambridge University Press
• Publication Date: 2007
• Language: English
• ISBN 10: 0521857724
• ISBN 13: 9780521857727
• Binding: Hardcover
• Condition: Very Good

About this title

Synopsis

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.

About the Author

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).