Experience Rating with Poisson Mixtures
Garfield Brown, Steve Brooks, Winston Buckley
TL;DR
This paper introduces a mixture Poisson model for claims counts, utilizing reversible jump MCMC to estimate the number of mixture components, enhancing flexibility in modeling claim frequency data.
Contribution
The paper's novelty lies in applying reversible jump MCMC to estimate the number of components in a mixture Poisson model for claims counts.
Findings
Effective estimation of mixture components using reversible jump MCMC.
Improved modeling of claims count data.
Potential for better risk assessment in insurance.
Abstract
We present a mixture Poisson model for claims counts in which the number of components in the mixture are estimated by reversible jump MCMC methods.
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Taxonomy
TopicsBayesian Methods and Mixture Models · Statistical Methods and Bayesian Inference · Probability and Risk Models