Preface; 1. Varieties of count data; 2. Poisson regression; 3. Testing overdispersion; 4. Assessment of fit; 5. Negative binomial regression; 6. Poisson inverse Gaussian regression; 7. Problems with zeros; 8. Modeling under-dispersed count data - generalized Poisson; 9. Complex data: more advanced models; Appendix A: SAS code; References; Index.
This book provides guidelines and fully worked examples of how to select, construct, interpret and evaluate the full range of count models.
Joseph Hilbe is a solar system ambassador with NASA's Jet Propulsion Laboratory, California Institute of Technology; an Adjunct Professor of Statistics at Arizona State University; an Emeritus Professor at the University of Hawaii; and a statistical modeling instructor for Statistics.com, a web-based continuing-education program in statistics. He is the author of several books on statistical modeling and serves as the coordinating editor for the Cambridge University Press series Predictive Analytics in Action.
'This is a first-rate introductory book for modeling count data, a
key challenge in applied statistics. Hilbe's experience and
affability shine in the text. His careful emphasis on establishing
the defensibility of models, for example, in the face of
overdispersion, will greatly benefit the beginning statistician.
His clear informal explanations of important and complicated
statistical principles are invaluable.' Andrew Robinson, University
of Melbourne
'The negative binomial model is the foundation for modern analysis
of count data. Joe Hilbe's work collects a vast wealth of technical
and practical information for the analyst. The theoretical
developments and thoroughly worked applications use realistic data
sets and a variety of computer packages. They will provide to the
practitioner an indispensable guide for basic single-equation count
data regressions and advanced applications with recently developed
model extensions and methods.' William Greene, New York
University
'This book is a great introduction to models for the analysis of
count data. Using the Poisson GLM as the basis, it covers a wide
range of modern extensions of GLMs, and this makes it unique.
Potentially complex models (which are often needed when analyzing
real data sets) are presented in an understandable way, partly
because data sets and software code are provided. I reckon that
this volume will be one of the standard GLM reference books for
many years to come.' Alain F. Zuur, Highland Statistics Ltd
'Modeling Count Data is a well-organized entry-level book mainly
written for applied researchers with little formal theoretical
background in statistics who need to analyse count data …
Thoroughly worked examples with software code, several of them
devoted to applying alternative count models to the same data set,
provide a basic guide for model selection among competing models.
The chapters are well structured, starting with points of
discussion and ending with a brief summary. Where required, section
themes are summarized. Also, the formula used, abbreviations used
and examples used are summarized in tabular form. In brief, it is a
remarkable book and can be used as a practical guide for
introducing count data analysis.' Anoop Chaturvedi, Journal of the
Royal Statistical Society
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