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).

• Preface; 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; References; Index.

'Students, developers, and practitioners in this area will all want to have this thorough guide close at hand. The wealth of theory and extensive applications using 'real' data sets and contemporary software will provide a crucial resource for their research.' William Greene, New York 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).

• Preface; 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; References; Index.

'Students, developers, and practitioners in this area will all want to have this thorough guide close at hand. The wealth of theory and extensive applications using 'real' data sets and contemporary software will provide a crucial resource for their research.' William Greene, New York 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).

• Preface; 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; References; Index.

'Students, developers, and practitioners in this area will all want to have this thorough guide close at hand. The wealth of theory and extensive applications using 'real' data sets and contemporary software will provide a crucial resource for their research.' William Greene, New York University