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Scale Mixtures Distributions in Insurance Applications

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  • Choy, S.T. Boris
  • Chan, C.M.

Abstract

In this paper non-normal distributions via scale mixtures are introduced into insurance applications. The symmetric distributions of interest are the Student-t and exponential power (EP) distributions. A Bayesian approach is adopted with the aid of simulation to obtain posterior summaries. We shall show that the computational burden for the Bayesian calculations is alleviated via the scale mixtures representations. Illustrative examples are given.

Suggested Citation

  • Choy, S.T. Boris & Chan, C.M., 2003. "Scale Mixtures Distributions in Insurance Applications," ASTIN Bulletin, Cambridge University Press, vol. 33(1), pages 93-104, May.
    • Handle: RePEc:cup:astinb:v:33:y:2003:i:01:p:93-104_01
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    Cited by:

    1. Wan, Wai-Yin & Chan, Jennifer So-Kuen, 2011. "Bayesian analysis of robust Poisson geometric process model using heavy-tailed distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 687-702, January.
    2. Victor Korolev & Alexander Zeifman, 2023. "Mixture Representations for Generalized Burr, Snedecor–Fisher and Generalized Student Distributions with Related Results," Mathematics, MDPI, vol. 11(18), pages 1-25, September.
    3. Benjamin Avanzi & Mark Lavender & Greg Taylor & Bernard Wong, 2022. "Detection and treatment of outliers for multivariate robust loss reserving," Papers 2203.03874, arXiv.org, revised Jun 2023.
    4. Klaus Müller & Wolf-Dieter Richter, 2019. "On p-generalized elliptical random processes," Journal of Statistical Distributions and Applications, Springer, vol. 6(1), pages 1-37, December.
    5. M. Arendarczyk & T. J. Kozubowski & A. K. Panorska, 2023. "Slash distributions, generalized convolutions, and extremes," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(4), pages 593-617, August.

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