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Toward a Unified Approach to Fitting Loss Models

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  • Stuart Klugman
  • Jacques Rioux

Abstract

There are four components to fitting models: selecting a set of candidate distributions, estimating parameters, evaluating the appropriateness of a model, and determining which member fits best. It is important to have the candidate set be small to avoid overfitting. Finite mixture models using a small number of base distributions provide an ideal set. Because actuaries fit models for a variety of situations, particularly with regard to data modifications, it is useful to have a single approach. Although not optimal or exact for a particular model or data structure, the method should be reasonable for most all settings. Such a method is proposed in this article.

Suggested Citation

  • Stuart Klugman & Jacques Rioux, 2006. "Toward a Unified Approach to Fitting Loss Models," North American Actuarial Journal, Taylor & Francis Journals, vol. 10(1), pages 63-83.
    • Handle: RePEc:taf:uaajxx:v:10:y:2006:i:1:p:63-83
    DOI: 10.1080/10920277.2006.10596240
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    Cited by:

    1. Tatjana Miljkovic & Daniel Fernández, 2018. "On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio," Risks, MDPI, vol. 6(2), pages 1-18, May.
    2. Blostein, Martin & Miljkovic, Tatjana, 2019. "On modeling left-truncated loss data using mixtures of distributions," Insurance: Mathematics and Economics, Elsevier, vol. 85(C), pages 35-46.
    3. Miljkovic, Tatjana & Grün, Bettina, 2016. "Modeling loss data using mixtures of distributions," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 387-396.

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