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Generalized Linear Models for Insurance Data

Author

Listed:
  • de Jong,Piet
  • Heller,Gillian Z.

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.

Suggested Citation

  • de Jong,Piet & Heller,Gillian Z., 2008. "Generalized Linear Models for Insurance Data," Cambridge Books, Cambridge University Press, number 9780521879149.
  • Handle: RePEc:cup:cbooks:9780521879149
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    Citations

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    Cited by:

    1. Shi, Peng & Feng, Xiaoping & Ivantsova, Anastasia, 2015. "Dependent frequency–severity modeling of insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 417-428.
    2. Liivika Tee & Meelis Käärik & Rauno Viin, 2017. "On Comparison of Stochastic Reserving Methods with Bootstrapping," Risks, MDPI, Open Access Journal, vol. 5(1), pages 1-21, January.
    3. Fuzi, Mohd Fadzli Mohd & Jemain, Abdul Aziz & Ismail, Noriszura, 2016. "Bayesian quantile regression model for claim count data," Insurance: Mathematics and Economics, Elsevier, vol. 66(C), pages 124-137.
    4. repec:gam:jrisks:v:4:y:2016:i:1:p:4:d:64467 is not listed on IDEAS
    5. Edward W. Frees & Gee Lee & Lu Yang, 2016. "Multivariate Frequency-Severity Regression Models in Insurance," Risks, MDPI, Open Access Journal, vol. 4(1), pages 1-36, February.
    6. Barbara Guardabascio & Marco Ventura, 2013. "Estimating the dose-response function through the GLM approach," German Stata Users' Group Meetings 2013 10, Stata Users Group.
    7. repec:gam:jrisks:v:6:y:2018:i:2:p:34-:d:140625 is not listed on IDEAS
    8. Yves L. Grize, 2015. "Applications of Statistics in the Field of General Insurance: An Overview," International Statistical Review, International Statistical Institute, vol. 83(1), pages 135-159, April.
    9. repec:prg:jnlpep:v:2017:y:2017:i:4:id:621:p:450-466 is not listed on IDEAS
    10. repec:wsi:apjorx:v:31:y:2014:i:05:n:s0217595914500328 is not listed on IDEAS
    11. Kudryavtsev, Andrey A., 2009. "Using quantile regression for rate-making," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 296-304, October.
    12. repec:cys:ecocyb:v:50:y:2017:i:4:p:91-107 is not listed on IDEAS
    13. Pinho, Luis Gustavo B. & Nobre, Juvêncio S. & Singer, Julio M., 2015. "Cook’s distance for generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 126-136.
    14. Sarabia, José María & Gómez-Déniz, Emilio & Prieto, Faustino & Jordá, Vanesa, 2016. "Risk aggregation in multivariate dependent Pareto distributions," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 154-163.
    15. Bortoluzzo, Adriana B. & Claro, Danny P. & Caetano, Marco Antonio L. & Artes, Rinaldo, 2009. "Estimating Claim Size and Probability in the Auto-insurance Industry: the Zero-adjusted Inverse Gaussian (ZAIG) Distribution," Insper Working Papers wpe_175, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    16. Claro, Danny P., 2009. "Estimating claim size and probability in the auto-insurance industry: the zeroadjusted Inverse Gaussian (ZAIG) distribution," Insper Working Papers wpe_159, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.
    17. William Guevara-Alarc'on & Luz Mery Gonz'alez & Armando Antonio Zarruk, 2017. "The partial damage loss cover ratemaking of the automobile insurance using generalized linear models," Papers 1707.03391, arXiv.org.
    18. Liangjun Su & Martin Spindler, 2013. "Nonparametric Testing for Asymmetric Information," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 208-225, April.
    19. Mihaela Covrig & Iulian Mircea & Gheorghita Zbaganu & Alexandru Coser & Alexandru Tindeche, 2015. "Using R In Generalized Linear Models," Romanian Statistical Review, Romanian Statistical Review, vol. 63(3), pages 33-45, September.
    20. Katrien Antonio & Emiliano Valdez, 2012. "Statistical concepts of a priori and a posteriori risk classification in insurance," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(2), pages 187-224, June.
    21. Avanzi, Benjamin & Wong, Bernard & Yang, Xinda, 2016. "A micro-level claim count model with overdispersion and reporting delays," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 1-14.
    22. Gómez-Déniz, E., 2016. "Bivariate credibility bonus–malus premiums distinguishing between two types of claims," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 117-124.
    23. Andreas Bayerstadler & Franz Benstetter & Christian Heumann & Fabian Winter, 2014. "A predictive modeling approach to increasing the economic effectiveness of disease management programs," Health Care Management Science, Springer, vol. 17(3), pages 284-301, September.
    24. Jiří Valecký, . "Calculation of Solvency Capital Requirements for Non-life Underwriting Risk Using Generalized Linear Models," Prague Economic Papers, University of Economics, Prague, vol. 0, pages 1-17.
    25. Tan, Chong It, 2016. "Varying transition rules in bonus–malus systems: From rules specification to determination of optimal relativities," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 134-140.

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