Jean-Francois Dupuy – författare

Statistical Methods for Overdispersed Count Data provides a review of the most recent methods and models for such data, including a description of R functions and packages that allow their implementation. All methods are illustrated on datasets arising in the field of health economics. As several tools have been developed to tackle over-dispersed and zero-inflated data (such as adjustment methods and zero-inflated models), this book covers the topic in a comprehensive and interesting manner.- Includes reading on several levels, including methodology and applications- Presents the state-of-the-art on the most recent zero-inflated regression models- Contains a single dataset that is used as a common thread for illustrating all methodologies- Includes R code that allows the reader to apply methodologies

Since they were first formulated in 1972, generalized linear models have enjoyed a veritable boom, with numerous applications in insurance, economics and biostatistics. Today, they are still the subject of a great deal of research.

This book provides an overview of the theory of generalized linear models. Particular attention is paid to the problems of censoring, missing data and excess zeros. Didactic and accessible, Generalized Linear Models is illustrated with exercises and numerous R codes.

With all the necessary prerequisites introduced in a step-by-step fashion, this book is aimed at students (at master''s or engineering school level), as well as teachers and practitioners of mathematics and statistical modeling.

Since they were first formulated in 1972, generalized linear models have enjoyed a veritable boom, with numerous applications in insurance, economics and biostatistics. Today, they are still the subject of a great deal of research.

This book provides an overview of the theory of generalized linear models. Particular attention is paid to the problems of censoring, missing data and excess zeros. Didactic and accessible, Generalized Linear Models is illustrated with exercises and numerous R codes.

With all the necessary prerequisites introduced in a step-by-step fashion, this book is aimed at students (at master''s or engineering school level), as well as teachers and practitioners of mathematics and statistical modeling.

Statistical Methods for Overdispersed Count Data provides a review of the most recent methods and models for such data, including a description of R functions and packages that allow their implementation. All methods are illustrated on datasets arising in the field of health economics. As several tools have been developed to tackle over-dispersed and zero-inflated data (such as adjustment methods and zero-inflated models), this book covers the topic in a comprehensive and interesting manner.

Includes reading on several levels, including methodology and applications Presents the state-of-the-art on the most recent zero-inflated regression models Contains a single dataset that is used as a common thread for illustrating all methodologies Includes R code that allows the reader to apply methodologies