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This paper proposes a kernel density estimation method for claim size distributions using shifted power transformation, improving power density and providing a more accurate estimation of claim sizes.

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Abstract

Key findings

  • The shifted power transformation improves the power density of the kernel density estimation method.
  • The proposed method outperforms existing methods in terms of accuracy and precision.
  • The method is applicable to various claim size distributions, including heavy-tailed distributions.

Keywords

Kernel density estimationKernel (algebra)Transformation (genetics)Multivariate kernel density estimationEstimationVariable kernel density estimation

Identifiers

DOI
10.21275/v5i4.nov163071
Journal
International Journal of Science and Research (IJSR)
Year
2016