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  1. Home
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  3. The Conway–Maxwell–Poisson Distribution
  • Cited by 8
Publisher:
Cambridge University Press
Online publication date:
March 2023
Print publication year:
2023
Online ISBN:
9781108646437
DOI:
https://doi.org/10.1017/9781108646437
Subjects:
Economics, Econometrics and Mathematical Methods, General Statistics and Probability, Statistical Theory and Methods, Statistics and Probability
Series:
Institute of Mathematical Statistics Monographs (8)
Information

Book description

While the Poisson distribution is a classical statistical model for count data, the distributional model hinges on the constraining property that its mean equal its variance. This text instead introduces the Conway-Maxwell-Poisson distribution and motivates its use in developing flexible statistical methods based on its distributional form. This two-parameter model not only contains the Poisson distribution as a special case but, in its ability to account for data over- or under-dispersion, encompasses both the geometric and Bernoulli distributions. The resulting statistical methods serve in a multitude of ways, from an exploratory data analysis tool, to a flexible modeling impetus for varied statistical methods involving count data. The first comprehensive reference on the subject, this text contains numerous illustrative examples demonstrating R code and output. It is essential reading for academics in statistics and data science, as well as quantitative researchers and data analysts in economics, biostatistics and other applied disciplines.

Reviews

‘This book will be a great resource for anyone interested in modeling or analyzing count data. It offers a comprehensive perspective on the Conway-Maxwell-Poisson distribution.'

Somnath Datta - University of Florida

‘This book is a terrific one-stop-shop for ‘everything COM-Poisson', not only integrating theoretical knowledge around the Conway-Maxwell Poisson distribution and its uses, but also providing practical notes and tips for applying the various relevant R libraries. Researchers and practitioners modeling count data or developing tools for modeling count data should find this book highly useful.'

Galit Shmueli - National Tsing Hua University

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Contents

Contents
  • 1 - Introduction: Count Data Containing Dispersion
    pp 1-21
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