Generalized Linear Models for Insurance Data

by Piet de Jong, Gillian Z. Heller

Overview

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.

Product Details

ISBN-13:	9781316099643
Publisher:	Cambridge University Press
Publication date:	02/28/2008
Series:	International Series on Actuarial Science
Sold by:	Barnes & Noble
Format:	eBook
Sales rank:	709,875
File size:	18 MB
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About the Author

Gillian Heller is Associate Professor of Statistics at Macquarie University. She has been teaching GLM to actuarial students for the past ten years, and has given several outside courses on GLMs to research analysts in insurance companies.
Piet de Jong is Professor of Actuarial Studies at Macquarie University. His research interests lie mainly in time series analysis and forecasting as well as actuarial areas, and he has consulted widely in the insurance and forecasting areas in both Australia and North America.

Table of Contents

Preface; 1. Insurance data; 2. Response distributions; 3. Exponential family responses and estimation; 4. Linear modeling; 5. Generalized linear models; 6. Models for count data; 7. Categorical responses; 8. Continuous responses; 9. Correlated data; 10. Extensions to the Generalized linear model; Appendix 1. Computer code and output; Bibliography; Index.

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

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

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