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Loss data analysis: Analysis of the sample dependence in density reconstruction by maxentropic methods

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  • Gomes-Gonçalves, Erika
  • Gzyl, Henryk
  • Mayoral, Silvia

Abstract

The problem of determining probability densities of positive random variables from empirical data is important in many fields, in particular in insurance and risk analysis. The method of maximum entropy has proven to be a powerful tool to determine probability densities from a few values of its Laplace transform. This is so even when the amount of data to compute numerically the Laplace transform is small. But in this case, the variability of the reconstruction due to the sample variability in the available data can lead to quite different results. It is the purpose of this note to quantify as much as possible the variability of the densities reconstructed by means of two maxentropic methods: the standard maximum entropy method and its extension to incorporate data with errors.

Suggested Citation

  • Gomes-Gonçalves, Erika & Gzyl, Henryk & Mayoral, Silvia, 2016. "Loss data analysis: Analysis of the sample dependence in density reconstruction by maxentropic methods," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 145-153.
    • Handle: RePEc:eee:insuma:v:71:y:2016:i:c:p:145-153
    DOI: 10.1016/j.insmatheco.2016.08.007
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    References listed on IDEAS

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    1. Gomes-Gonçalves, Erika & Gzyl, Henryk & Mayoral, Silvia, 2015. "Two maxentropic approaches to determine the probability density of compound risk losses," Insurance: Mathematics and Economics, Elsevier, vol. 62(C), pages 42-53.
    2. den Iseger, P. W. & Smith, M. A. J. & Dekker, R., 1997. "Computing compound distributions faster!," Insurance: Mathematics and Economics, Elsevier, vol. 20(1), pages 23-34, June.
    3. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    4. Gomes-Gonçalves, Erika & Gzyl, Henryk & Mayoral, Silvia, 2015. "Maxentropic approach to decompound aggregate risk losses," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 326-336.
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