Risk measure estimation under two component mixture models with trimmed data

Document Type

Article

Publication Date

1-1-2019

Abstract

Several two component mixture models from the transformed gamma and transformed beta families are developed to assess risk performance. Their common statistical properties are given and applications to real insurance loss data are shown. A new data trimming approach for parameter estimation is proposed using the maximum likelihood estimation method. Assessment with respect to Value-at-Risk and Conditional Tail Expectation risk measures are presented. Of all the models examined, the mixture of inverse transformed gamma-Burr distributions consistently provides good results in terms of goodness-of-fit and risk estimation in the context of the Danish fire loss data. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

Danish fire loss data, heavy tailed distributions, mixture models, transformed gamma and transformed beta families

Divisions

MathematicalSciences

Funders

Ministry of Higher Education, Malaysia under Fundamental Research Grant Scheme (FRGS) FP040-2017A

Publication Title

Journal of Applied Statistics

Volume

46

Issue

5

Publisher

Taylor & Francis

This document is currently not available here.

Share

COinS