Multivariate density estimation using dimension reducing information and tail flattening transformations

Tine Buch-Kromann, Montserrat Guillén, Oliver Linton, Jens Perch Nielsen

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)

Abstract

We propose a nonparametric multiplicative bias corrected transformation estimator designed for heavy tailed data. The multiplicative correction is based on prior knowledge and has a dimension reducing effect at the same time as the original dimension of the estimation problem is retained. Adding a tail flattening transformation improves the estimation significantly-particularly in the tail-and provides significant graphical advantages by allowing the density estimation to be visualized in a simple way. The combined method is demonstrated on a fire insurance data set and in a data-driven simulation study.

Original languageEnglish
Pages (from-to)99-110
Number of pages12
JournalInsurance: Mathematics and Economics
Volume48
Issue number1
DOIs
Publication statusPublished - Jan 2011
Externally publishedYes

Keywords

  • Bias reduction
  • Kernel
  • Multiplicative correction

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