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Modeling Hidden Exposures in Claim Severity Via the Em Algorithm

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  • Grzegorz Rempala
  • Richard Derrig

Abstract

We consider the issue of modeling the latent or hidden exposure occurring through either incomplete data or an unobserved underlying risk factor. We use the celebrated expectationmaximization (EM) algorithm as a convenient tool in detecting latent (unobserved) risks in finite mixture models of claim severity and in problems where data imputation is needed. We provide examples of applicability of the methodology based on real-life auto injury claim data and compare, when possible, the accuracy of our methods with that of standard techniques. Sample data and an EM algorithm program are included to allow readers to experiment with the EM methodology themselves.

Suggested Citation

  • Grzegorz Rempala & Richard Derrig, 2005. "Modeling Hidden Exposures in Claim Severity Via the Em Algorithm," North American Actuarial Journal, Taylor & Francis Journals, vol. 9(2), pages 108-128.
    • Handle: RePEc:taf:uaajxx:v:9:y:2005:i:2:p:108-128
    DOI: 10.1080/10920277.2005.10596206
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    Cited by:

    1. Shengkun Xie, 2019. "Defining Geographical Rating Territories in Auto Insurance Regulation by Spatially Constrained Clustering," Risks, MDPI, vol. 7(2), pages 1-20, April.
    2. Ajit Chaturvedi & Sudeep R. Bapat & Neeraj Joshi, 2022. "Sequential Estimation of an Inverse Gaussian Mean with Known Coefficient of Variation," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 402-420, May.

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