Kimberly F. Sellers

Conway-Maxwell-Poisson Distribution (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 22. Februar 2023
• Englisch
• ISBN-13: 9781108576345
• Artikelnr.: 68969872

Autorenporträt

Kimberly F. Sellers is Professor in the Department of Mathematics and Statistics at Georgetown University, and a Principal Researcher with the Center for Statistical Research and Methodology at the US Census Bureau in Washington, DC. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.

Inhaltsangabe

Preface
1. Introduction: count data containing dispersion
2. The Conway-Maxwell-Poisson (COM-Poisson) distribution
3. Distributional extensions and generalities
4. Multivariate forms of the COM-Poisson distribution
5. COM-Poisson regression
6. COM-Poisson control charts
7. COM-Poisson models for serially dependent count data
8. COM-Poisson cure rate models
Bibliography
Index.