Generalized Linear Models for Insurance Data

Piet de Jong; Gillian Z. Heller

doi:10.1017/CBO9780511755408

This is the only book actuaries need to understand generalized linear models (GLMs) for insurance applications. GLMs are used in the insurance industry to support critical decisions. Until now, no text has introduced GLMs in this context or addressed the problems specific to insurance data. Using insurance data sets, this practical, rigorous book treats GLMs, covers all standard exponential family distributions, extends the methodology to correlated data structures, and discusses recent developments which go beyond the GLM. The issues in the book are specific to insurance data, such as model selection in the presence of large data sets and the handling of varying exposure times. Exercises and data-based practicals help readers to consolidate their skills, with solutions and data sets given on the companion website. Although the book is package-independent, SAS code and output examples feature in an appendix and on the website. In addition, R code and output for all the examples are provided on the website.

"I would recommend such a book to my students without hesitation."
Cho-Jieh Chen, Journal of the American Statistical Association

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Generalized Linear Models for Insurance Data

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Contents

Frontmatter
pp i-iv

Contents
pp v-viii

Preface
pp ix-x

1 - Insurance data
pp 1-19

2 - Response distributions
pp 20-34

3 - Exponential family responses and estimation
pp 35-41

4 - Linear modeling
pp 42-63

5 - Generalized linear models
pp 64-80

6 - Models for count data
pp 81-96

7 - Categorical responses
pp 97-119

8 - Continuous responses
pp 120-128

9 - Correlated data
pp 129-140

10 - Extensions to the generalized linear model
pp 141-149

Appendix 1 - Computer code and output
pp 150-191

Bibliography
pp 192-194

Index
pp 195-196

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