Abstract
This paper demonstrates an approach to analyzing liability data recently developed by a Danish insurance company. The approach is based on a Champernowne distribution, which is corrected with a non-parametric estimator. The correction estimator is obtained by transforming the data set with the estimated modified Champernowne cdf and then estimating the density of the transformed data set by using the classical kernel density estimator. Our approach is illustrated by applying it to an actual data set.
Key words and phrases: Semiparametric kernel density estimator, corrected modified Champernowne method, heavy-tailed distributions, Champernowne distribution, extreme value theory, generalized Pareto distribution
Corresponding Author:
Tine Buch-Kromann
Codan Insurance
Gammel Kongevej 60
DK-1790
E-mail: tbl@codan.dk
© Copyright Absalom Press, Inc.