Kernel density estimation for heavy-tailed distributions using the champernowne transformation

@inproceedings{BUCHLARSEN2005KernelDE,
  title={Kernel density estimation for heavy-tailed distributions using the champernowne transformation},
  author={TINE BUCH-LARSEN and Jens Perch Nielsen and Montserrat Guillen and Catalina Bolanc{\'e}},
  year={2005}
}
When estimating loss distributions in insurance, large and small losses are usually split because it is difficult to find a simple parametric model that fits all claim sizes. This approach involves determining the threshold level between large and small losses. In this article, a unified approach to the estimation of loss distributions is presented. We propose an estimator obtained by transforming the data set with a modification of the Champernowne cdf and then estimating the density of the… CONTINUE READING
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