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Negative Binomial Regression, by Joseph M. Hilbe
PDF Download Negative Binomial Regression, by Joseph M. Hilbe
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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 codes are provided throughout the text, with additional code (plus SAS), derivations and data provided on the book's website. Written for the practising 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.
- Sales Rank: #688674 in Books
- Published on: 2011-03-08
- Original language: English
- Number of items: 1
- Dimensions: 8.98" h x 1.18" w x 5.98" l, 2.10 pounds
- Binding: Hardcover
- 576 pages
Review
"As with all of Joe Hilbe's books this text is thorough and scholarly with an extensive list of references. The text is well-written and for the most part easy to understand."
Michael R. Chernick, Significance
"For any applied statistician who needs negative binomial models in their research and applications, it is usually not easy to find a book to provide both theoretical fundamentals and practical expert insights. This books meets such a need perfectly. Overall, this is a very well-written book. Its statistical rigor and expert insights in negative binomial modeling should be very appealing to readers of Technometrics."
Xianggui Qu, Oakland University for Technometrics
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).
Most helpful customer reviews
12 of 13 people found the following review helpful.
A good book made better
By Michael R. Chernick
The first edition of this book was one of the first on this topic. The text is is very comprehensive, covering count models in general, the common Poisson regression model and its generalization to over-and-under dispersion with all the various forms of the negative binomial regression model. The Poisson distribution has the property that its mean and variance are the same. When sample estimates of variance are significantly higher (lower) than the estimated mean, the model is said to be overdispersed (underdispersed).
The main additions in the second edition of the book are the advances in software to estimate parameters of the various negative binomial models. Hilbe describes the currently available software in SAS, SPSS and STATA as well as the econometric package LIMDEP.
The book covers the historical development of the negative binomial regression model. It is primarily an applied text with numerous examples and demonstration of the various software products. As with all of Joe Hilbe's books, this text is thorough and scholarly with an extensive list of references. Important theorems and other theoretical results are given but are presented to be imformative rather than to develop and teach the theory. The text is well-written and for the most part easy to understand. Emphasis is on computation and goodness of fit of the models. Although both overdispersion and underdispersion are covered overdispersion is emphasized as Hilbe sees it as the most common departure from the Poisson model.
3 of 3 people found the following review helpful.
If you work with count data, you'll want this book
By C. Andersen
If you work professionally with count or count-like data, as I do, this is a book you'll want to add to you library and treat as a major reference. You may have had courses in regression or categorical analysis which introduced you to Poisson or even negative binomial models for count data, but this is the book which will fill-in the gaps, tell you what assumptions really need checked, and how to validate and interpret the results. This book starts with Poisson models, expands to negative binomial models, and generalizes to zero-truncated, zero-inflated, hurdle models, and beyond. Hilbe manages to cover all of this with very readable -- even conversational -- prose. If you need deep theory, it is here (though you can skip it), and if you want to be able generate synthetic data to explore and validate your models, that's covered too. This book has joined perhaps a half-dozen books in my library that I consider among the more valuable references. Based upon my experience with this book, I subsequently bought Hilbe's book on Logistic Regression Models -- which seems likewise well-done.
EDIT: Amazon is associating the review with the Kindle edition; actually it applies to the print edition.
2 of 2 people found the following review helpful.
"Go-to" book for NBR
By Ian Dohoo
This book is my "go to" book for negative binomial regression. Prof. Hilbe is the author of many of the programs (in both R and Stata) for specialized forms of NBR (eg heterogenous NBR) so he clearly knows the technical aspects of the subject. However, what made the book particularly useful for me was his ability to present complex topics in a clear and understandable way and to back that up with well worked examples. As one specific example, I found his presentation of overdispersion (and the distinction between real and apparent overdispersion) very illuminating. Certainly, as I was preparing the chapter of "Methods in Epidemiologic Research" [...] that deals with the analysis of count data, this book was my primary reference text. If you need to analyze count data and envision using anything more complex than a simple Poisson regression, you should get a copy of this text.
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