Generalized Linear Models for Insurance Data
Generalized Linear Models for Insurance Data by Piet de Jong, published by Cambridge University Press on February 28, 2008, is an illustrated edition comprising 208 pages. This book serves as a comprehensive resource for actuaries seeking to understand generalized linear models (GLMs) specifically tailored for insurance applications. It addresses unique challenges associated with insurance data, providing a practical and rigorous approach to the subject.
Readers will find a thorough exploration of GLMs, including discussions on standard exponential family distributions and methodologies for correlated data structures. The book emphasizes model selection in large data sets and varying exposure times, making it particularly relevant for professionals in the insurance industry. Exercises and practical data applications are included to reinforce learning, with solutions and data sets available on a companion website. Additionally, the book features SAS code and R code for examples, enhancing its utility for practitioners in the field of statistics and mathematics.
Official synopsis Publisher
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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