Book contents
- Frontmatter
- Dedication
- Contents
- Figures
- Tables
- Preface
- Acknowledgments
- 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
- References
- Index
3 - Distributional Extensions and Generalities
Published online by Cambridge University Press: 02 March 2023
- Frontmatter
- Dedication
- Contents
- Figures
- Tables
- Preface
- Acknowledgments
- 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
- References
- Index
Summary
The Conway–Maxwell–Poisson distribution has garnered interest in and development of other flexible alternatives to classical distributions. This chapter introduces various distributional extensions and generalities motivated by functions of COM–Poisson random variables, including Conway–Maxwell-inspired generalizations of the Skellam distribution, binomial distribution, negative binomial distribution, the Katz class of distributions, two flexible series system life length distributions, and generalizations of the negative hypergeometric distribution.
Keywords
- Type
- Chapter
- Information
- The Conway–Maxwell–Poisson Distribution , pp. 71 - 123Publisher: Cambridge University PressPrint publication year: 2023