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Generalized Linear Models for Insurance Data
Abstract
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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“A preliminary examination of the data (not included here but available, for example, in De Jong and Heller (2008)), show that the distribution of claims is skewed.”
From The Discriminating (Pricing) Actuary (2023) · cites this paper as De Jong and Heller (2008)
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Henckaerts, R., Côté, M.-P., Antonio, K., & Verbelen, R. (2021) · North American Actuarial Journal · doi:10.1080/10920277.2020.1745656 · Cited by 109
The Discriminating (Pricing) Actuary
Frees, E. W. (., & Huang, F. (2023) · North American Actuarial Journal · doi:10.1080/10920277.2021.1951296 · Cited by 56
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Metadata
- Authors
- de Jong, P., & Heller, G. Z.
- Publisher
- Cambridge University Press
- Published
- 2008-02-28
- Cited by
- 263
- References
- 0
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- CoChat. (2026). Citation check: Generalized Linear Models for Insurance Data. https://app.cochat.ai/papers/10.1017/cbo9780511755408
- The paper
- de Jong, P., & Heller, G. Z. (2008). Generalized Linear Models for Insurance Data. https://doi.org/10.1017/cbo9780511755408