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Risk measure estimation under two component mixture models with trimmed data

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 propo...

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Main Authors: Bakar, Shaiful Anuar Abu, Nadarajah, Saralees
Format: Article
Published: Taylor & Francis 2019
Subjects:
QA Mathematics
Online Access:http://eprints.um.edu.my/24293/
https://doi.org/10.1080/02664763.2018.1517146
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spelling my.um.eprints.242932020-05-18T02:28:00Z http://eprints.um.edu.my/24293/ Risk measure estimation under two component mixture models with trimmed data Bakar, Shaiful Anuar Abu Nadarajah, Saralees QA Mathematics 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. Taylor & Francis 2019 Article PeerReviewed Bakar, Shaiful Anuar Abu and Nadarajah, Saralees (2019) Risk measure estimation under two component mixture models with trimmed data. Journal of Applied Statistics, 46 (5). pp. 835-852. ISSN 0266-4763 https://doi.org/10.1080/02664763.2018.1517146 doi:10.1080/02664763.2018.1517146
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA Mathematics
spellingShingle QA Mathematics
Bakar, Shaiful Anuar Abu
Nadarajah, Saralees
Risk measure estimation under two component mixture models with trimmed data
description 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.
format Article
author Bakar, Shaiful Anuar Abu
Nadarajah, Saralees
author_facet Bakar, Shaiful Anuar Abu
Nadarajah, Saralees
author_sort Bakar, Shaiful Anuar Abu
title Risk measure estimation under two component mixture models with trimmed data
title_short Risk measure estimation under two component mixture models with trimmed data
title_full Risk measure estimation under two component mixture models with trimmed data
title_fullStr Risk measure estimation under two component mixture models with trimmed data
title_full_unstemmed Risk measure estimation under two component mixture models with trimmed data
title_sort risk measure estimation under two component mixture models with trimmed data
publisher Taylor & Francis
publishDate 2019
url http://eprints.um.edu.my/24293/
https://doi.org/10.1080/02664763.2018.1517146
_version_ 1669007988225998848
score 13.251813

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