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Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models

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When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero-inflated, hurdle and compound frequency models are typically applied to insurance data to account for such a feature of the data. However, a natural way to deal with unobserved heterogeneity is to consider mixtures of a simpler models. In this paper, we consider k-finite mixtures of some typical regression models. (...)

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Articles publicats en revistes (Matemàtica Econòmica, Financera i Actuarial)

# metadata.dc.title[].value
Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models