Generalized Linear Models for Insurance Data

Piet de Jong, Gillian Z. Heller

ISBN 10: 0521879140 / ISBN 13: 9780521879149

Published by Cambridge University Press, GB, 2008

About this Item

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.

Seller Inventory # LU-9780521879149

Bibliographic Details

• Title: Generalized Linear Models for Insurance Data
• Publisher: Cambridge University Press, GB
• Publication Date: 2008
• Language: English
• ISBN 10: 0521879140
• ISBN 13: 9780521879149
• Binding: Hardback
• Condition: New
• Weight: 430 grams

About this title

Synopsis

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.

About the Authors

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.