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
Students in statistics, biostatistics, econometrics and professional statisticians with interest in the analysis of count data; Non-statisticians with skills in R softwares (economists, decision-makers in public health…)
- Generalized Linear Models
2. Over-Scattered Count Data
3. Count Data and Inflation of Zeros
- No. of pages:
- © ISTE Press - Elsevier 2019
- 1st October 2018
- ISTE Press - Elsevier
- Hardcover ISBN:
Jean-Francois Dupuy is a Professor at the INSA Rennes since 2011. From 2009 to 2011, he was a Professor at the University La Rochelle in France. In 2002 he obtained a PhD in Applied Mathematics from the University Paris-Descartes.
INSA Rennes, France