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Negative Binomial Regression

2nd Edition

$95.00 (P)

  • Author: Joseph M. Hilbe, Jet Propulsion Laboratory, California Institute of Technology; and Arizona State University
  • Date Published: April 2011
  • availability: Available
  • format: Hardback
  • isbn: 9780521198158

$ 95.00 (P)

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About the Authors
  • 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.

    • Fully revised to incorporate recent software developments
    • Every model currently offered in a commercial statistical software package is discussed in detail
    • Data sets and additional code available on a companion website
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    Reviews & endorsements

    "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

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    Product details

    • Edition: 2nd Edition
    • Date Published: April 2011
    • format: Hardback
    • isbn: 9780521198158
    • length: 572 pages
    • dimensions: 231 x 155 x 36 mm
    • weight: 1.13kg
    • contains: 36 b/w illus. 170 tables
    • availability: Available
  • Table of Contents

    1. Introduction
    2. The concept of risk
    3. Overview of count response models
    4. Methods of estimation and assessment
    5. Assessment of count models
    6. Poisson regression
    7. Overdispersion
    8. Negative binomial regression
    9. Negative binomial regression: modeling
    10. Alternative variance parameterizations
    11. Problems with zero counts
    12. Censored and truncated count models
    13. Handling endogeneity and latent class models
    14. Count panel models
    15. Bayesian negative binomial models
    Appendix A. Constructing and interpreting interactions
    Appendix B. Data sets and Stata files

  • Resources for

    Negative Binomial Regression

    Joseph M. Hilbe

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  • Instructors have used or reviewed this title for the following courses

    • Applied Data Analysis
    • Psychological Statistics ll
    • Statistics for Demographic Data 2
  • Author

    Joseph M. Hilbe, Jet Propulsion Laboratory, California Institute of Technology; and Arizona State University
    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).

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