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Bayesian Assessment of the Distribution of Insurance Claim Counts Using Reversible Jump MCMC

Author

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  • Ioannis Ntzoufras
  • Athanassios Katsis
  • Dimitris Karlis

Abstract

The aim of this paper is to construct Bayesian model comparison tests between discrete distributions used for claim count modeling in the actuarial field. We use advanced computational techniques to estimate the posterior model odds among different distributions for claim counts. We construct flexible reversible jump Markov Chain Monte Carlo algorithms and implement them in various illustrated examples.

Suggested Citation

  • Ioannis Ntzoufras & Athanassios Katsis & Dimitris Karlis, 2005. "Bayesian Assessment of the Distribution of Insurance Claim Counts Using Reversible Jump MCMC," North American Actuarial Journal, Taylor & Francis Journals, vol. 9(3), pages 90-108.
    • Handle: RePEc:taf:uaajxx:v:9:y:2005:i:3:p:90-108
    DOI: 10.1080/10920277.2005.10596213
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    Cited by:

    1. Perrakis, Konstantinos & Ntzoufras, Ioannis & Tsionas, Efthymios G., 2014. "On the use of marginal posteriors in marginal likelihood estimation via importance sampling," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 54-69.
    2. Mohammadreza Mohebbi & Rory Wolfe & Andrew Forbes, 2014. "Disease Mapping and Regression with Count Data in the Presence of Overdispersion and Spatial Autocorrelation: A Bayesian Model Averaging Approach," IJERPH, MDPI, vol. 11(1), pages 1-20, January.
    3. Streftaris, George & Worton, Bruce J., 2008. "Efficient and accurate approximate Bayesian inference with an application to insurance data," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2604-2622, January.

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