Statistical Methods for Overdispersed Count Data
- 1st Edition - November 21, 2018
- Latest edition
- Author: Jean-Francois Dupuy
- Language: English
- Hardback ISBN:9 7 8 - 1 - 7 8 5 4 8 - 2 6 6 - 3
- eBook ISBN:9 7 8 - 0 - 0 8 - 1 0 2 3 7 4 - 7
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… Read more
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 (e.g. economists, decision-makers in public health)
1. A Brief Overview of Linear Models
2. Generalized Linear Models
3. Overdispersion in Count Data
4. Count Data and Zero Inflation
- Edition: 1
- Latest edition
- Published: November 21, 2018
- Language: English
JD
Jean-Francois Dupuy
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.
Affiliations and expertise
INSA Rennes, FranceRead Statistical Methods for Overdispersed Count Data on ScienceDirect