This is the first comprehensive introduction to the Conway-Maxwell-Poisson distribution and its contributions in statistical theory and computing in R, including its uses in count data modelling. An essential reference for academics in statistics and data science, as well as quantitative researchers and data analysts in applied disciplines.
This is the first comprehensive introduction to the Conway-Maxwell-Poisson distribution and its contributions in statistical theory and computing in R, including its uses in count data modelling. An essential reference for academics in statistics and data science, as well as quantitative researchers and data analysts in applied disciplines.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
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.
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