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• W2171100500 abstract "Abstract We propose a Poisson mixture model for count data to determine the number of groups in a Group Life insurance portfolio consisting of claim numbers or deaths. We take a non-parametric Bayesian approach to modelling this mixture distribution using a Dirichlet process prior and use reversible jump Markov chain Monte Carlo to estimate the number of components in the mixture. Unlike Haastrup, we show that the assumption of identical heterogeneity for all groups may not hold as 88% of the posterior probability is assigned to models with two or three components, and 11% to models with four or five components, whereas models with one component are never visited. Our major contribution is showing how to account for both model uncertainty and parameter estimation within a single framework." @default.
• W2171100500 created "2016-06-24" @default.
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• W2171100500 date "2015-07-16" @default.
• W2171100500 modified "2023-09-27" @default.
• W2171100500 title "Experience rating with Poisson mixtures" @default.
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• W2171100500 doi "https://doi.org/10.1017/s1748499515000019" @default.
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