Abstract
Data sets exhibiting a hierarchical or nested structure, or including longitudinal or spatial elements often arise in insurance studies. This generally results in correlation among the responses within the same group, casting doubts about the outputs of analyses assuming mutual independence. Random effects offer a convenient way to model such grouping structure. This chapter presents the Generalized Linear Mixed Model (GLMM) approach to regression analysis. In this framework, random effects are added on the same scale as the linear combination of the available features (called fixed effects). Predictive distributions, that is, conditional distribution of the response given past experience, are particularly attractive to re-valuate future premiums based on claims observed previously.
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Notes
- 1.
NACE codes are often used in workers’ compensation insurance. NACE (Nomenclature of Economic Activities) is the European statistical classification of economic activities grouping organizations according to their business activities. The NACE code is subdivided in a hierarchical, four-level structure.
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Denuit, M., Hainaut, D., Trufin, J. (2019). Over-Dispersion, Credibility Adjustments, Mixed Models, and Regularization. In: Effective Statistical Learning Methods for Actuaries I. Springer Actuarial(). Springer, Cham. https://doi.org/10.1007/978-3-030-25820-7_5
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DOI: https://doi.org/10.1007/978-3-030-25820-7_5
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