Modeling Count Data: A Comprehensive Guide to Poisson, Negative Binomial & Overdispersion Models by Joseph M. Hilbe

Introduction

Welcome to a rigorous yet accessible journey into the world of count data modeling. Whether you are a postgraduate student in statistics, a researcher in the life sciences, or a data analyst working in econometrics or transportation, this book is designed to equip you with the tools you need to handle discrete, non-negative integer outcomes with confidence. Written by a leading authority in statistical modeling, this hardcover edition from Cambridge University Press is an essential addition to any serious academic or professional library in India.

Book Overview

Modeling Count Data by Joseph M. Hilbe offers a clear, step-by-step introduction to the analysis of count response variables. The book begins with the foundational Poisson model and systematically progresses through overdispersion, negative binomial regression, zero-inflated models, hurdle models, and other extensions. Each concept is illustrated with practical examples and annotated code in Stata, R, and SAS, making it easy for readers to apply the methods to their own datasets. The text is structured to be self-contained, assuming only a basic familiarity with regression and probability theory.

Key Highlights

• Comprehensive coverage – from Poisson regression to advanced count models, including zero-truncated, zero-inflated, and multilevel count models.
• Practical code examples – ready-to-run programs in Stata, R, and SAS that you can adapt for your own research.
• Real-world datasets – numerous case studies from health, ecology, economics, and transportation demonstrate the application of each model.
• Clear pedagogical design – tables, insets, and bullet-point summaries help you grasp key concepts quickly.
• Focus on interpretation – learn how to present and communicate your results effectively to both technical and non-technical audiences.

Inside the Book

The book is organized into twelve well-structured chapters. Early chapters provide a refresher on essential statistical concepts and introduce the Poisson model with detailed diagnostics. Subsequent chapters tackle overdispersion, negative binomial models, and the various modifications needed when data exhibit excess zeros, truncation, or clustering. Each chapter includes exercises and solutions, making it ideal for classroom use or self-study. The final chapter offers guidance on model selection and validation, ensuring you can confidently choose the right model for your data.

Key Topics

• Poisson regression and its assumptions
• Overdispersion detection and negative binomial models
• Zero-inflated and hurdle models
• Truncated count models
• Multilevel and longitudinal count data
• Model fit assessment and comparison
• Bayesian approaches to count data
• Simulation and power analysis for count outcomes

Reader Benefits

By studying this book, you will gain the ability to critically evaluate published research that uses count data, design your own studies with appropriate sample sizes, and produce reproducible analyses using open-source or commercial software. The emphasis on practical implementation means you can start applying the methods immediately, even if you have limited programming experience. For Indian researchers, the examples drawn from epidemiology, agricultural statistics, and econometrics are particularly relevant to local contexts.

Learning Outcomes

• Understand the fundamental differences between continuous and count data models.
• Fit and interpret Poisson and negative binomial models using Stata, R, or SAS.
• Diagnose and correct for overdispersion, zero-inflation, and data truncation.
• Select the most appropriate count model for a given research question.
• Communicate statistical findings clearly in reports and publications.
• Apply advanced techniques such as multilevel and longitudinal count modeling.

Who Should Read

This book is ideal for graduate students in statistics, biostatistics, econometrics, and data science. It is also highly valuable for practicing researchers in public health, ecology, transportation engineering, and any field where count responses are common. The accessible writing style makes it suitable for those with only one or two semesters of introductory statistics. Professors will find it a perfect textbook for a one-semester course on count data analysis.

About the Author

Joseph M. Hilbe is a renowned statistician and author, known for his pioneering work in count data modeling. He is a Professor Emeritus at Arizona State University and an adjunct professor at the University of Hawaii. With over 40 years of experience, he has authored multiple influential texts, including Negative Binomial Regression and Logistic Regression Models. His clear, example-driven teaching style has helped thousands of researchers worldwide master complex statistical methods.

About the Publisher

Cambridge University Press is a world-leading academic publisher with a rich history dating back to 1534. Known for its rigorous peer-review process and high-quality scholarly works, CUP publishes textbooks and reference materials that set the standard in their fields. This hardcover edition is printed on acid-free paper and bound to withstand years of heavy use in libraries and labs across India.

Conclusion

Modeling Count Data is more than a textbook—it is a practical companion for anyone who works with counts. With its blend of theory, code, and real-world examples, it empowers you to move beyond simple Poisson models and embrace the full richness of modern count data analysis. Order your copy today from Bookshops.in and take your statistical skills to the next level.