Modeling and Evaluating Insurance Losses Via Mixtures of Erlang Distributions

Simon Lee and X. Lin

North American Actuarial Journal, 2010, vol. 14, issue 1, 107-130

Abstract: In this paper we suggest the use of mixtures of Erlang distributions with common scale parameter to model insurance losses. A modified expectation-maximization (EM) algorithm for parameter estimation tailored to this class of distributions is presented, and its computation efficiency is discussed. Goodness-of-fit tests are performed for data generated from some common parametric distributions and for catastrophic loss data in the United States. Formulas for value-at-risk and conditional tail expectation are provided for individual and aggregate losses.

Date: 2010
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DOI: 10.1080/10920277.2010.10597580

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