Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models

When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero...

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Detalles Bibliográficos
Autores: Bermúdez, Lluís, Karlis, Dimitris, Morillo, Isabel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Barcelona
Repositorio:Dipòsit Digital de la UB
OAI Identifier:oai:diposit.ub.edu:2445/149148
Acceso en línea:https://hdl.handle.net/2445/149148
Access Level:acceso abierto
Palabra clave:Anàlisi de regressió
Variables (Matemàtica)
Assegurances d'automòbils
Regression analysis
Variables (Mathematics)
Automobile insurance
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spelling Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture ModelsBermúdez, LluísKarlis, DimitrisMorillo, IsabelAnàlisi de regressióVariables (Matemàtica)Assegurances d'automòbilsRegression analysisVariables (Mathematics)Automobile insuranceWhen modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero-inflated, hurdle and compound frequency models are typically applied to insurance data to account for such a feature of the data. However, a natural way to deal with unobserved heterogeneity is to consider mixtures of a simpler models. In this paper, we consider k-finite mixtures of some typical regression models. (...)MDPI2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/2445/149148Articles publicats en revistes (Matemàtica Econòmica, Financera i Actuarial)reponame:Dipòsit Digital de la UBinstname:Universidad de BarcelonaInglésReproducció del document publicat a: https://doi.org/10.3390/risks8010010Risks , 2020, vol. 8, num. 1(10), p. 01-13https://doi.org/10.3390/risks8010010cc-by (c) Bermúdez, Lluís et al., 2020http://creativecommons.org/licenses/by/3.0/esinfo:eu-repo/semantics/openAccessoai:diposit.ub.edu:2445/1491482026-05-27T06:46:51Z
dc.title.none.fl_str_mv Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models
title Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models
spellingShingle Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models
Bermúdez, Lluís
Anàlisi de regressió
Variables (Matemàtica)
Assegurances d'automòbils
Regression analysis
Variables (Mathematics)
Automobile insurance
title_short Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models
title_full Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models
title_fullStr Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models
title_full_unstemmed Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models
title_sort Modelling Unobserved Heterogeneity in Claim Counts Using Finite Mixture Models
dc.creator.none.fl_str_mv Bermúdez, Lluís
Karlis, Dimitris
Morillo, Isabel
author Bermúdez, Lluís
author_facet Bermúdez, Lluís
Karlis, Dimitris
Morillo, Isabel
author_role author
author2 Karlis, Dimitris
Morillo, Isabel
author2_role author
author
dc.subject.none.fl_str_mv Anàlisi de regressió
Variables (Matemàtica)
Assegurances d'automòbils
Regression analysis
Variables (Mathematics)
Automobile insurance
topic Anàlisi de regressió
Variables (Matemàtica)
Assegurances d'automòbils
Regression analysis
Variables (Mathematics)
Automobile insurance
description When modelling insurance claim count data, the actuary often observes overdispersion and an excess of zeros that may be caused by unobserved heterogeneity. A common approach to accounting for overdispersion is to consider models with some overdispersed distribution as opposed to Poisson models. Zero-inflated, hurdle and compound frequency models are typically applied to insurance data to account for such a feature of the data. However, a natural way to deal with unobserved heterogeneity is to consider mixtures of a simpler models. In this paper, we consider k-finite mixtures of some typical regression models. (...)
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/149148
url https://hdl.handle.net/2445/149148
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Reproducció del document publicat a: https://doi.org/10.3390/risks8010010
Risks , 2020, vol. 8, num. 1(10), p. 01-13
https://doi.org/10.3390/risks8010010
dc.rights.none.fl_str_mv cc-by (c) Bermúdez, Lluís et al., 2020
http://creativecommons.org/licenses/by/3.0/es
info:eu-repo/semantics/openAccess
rights_invalid_str_mv cc-by (c) Bermúdez, Lluís et al., 2020
http://creativecommons.org/licenses/by/3.0/es
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Articles publicats en revistes (Matemàtica Econòmica, Financera i Actuarial)
reponame:Dipòsit Digital de la UB
instname:Universidad de Barcelona
instname_str Universidad de Barcelona
reponame_str Dipòsit Digital de la UB
collection Dipòsit Digital de la UB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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