Generalized Linear Models for Insurance Data

Business & Finance, Economics, Statistics, Nonfiction, Science & Nature, Mathematics
Cover of the book Generalized Linear Models for Insurance Data by Piet de Jong, Gillian Z. Heller, Cambridge University Press
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Author: Piet de Jong, Gillian Z. Heller ISBN: 9781316099643
Publisher: Cambridge University Press Publication: February 28, 2008
Imprint: Cambridge University Press Language: English
Author: Piet de Jong, Gillian Z. Heller
ISBN: 9781316099643
Publisher: Cambridge University Press
Publication: February 28, 2008
Imprint: Cambridge University Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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