Copula-based bivariate finite mixture regression models with an application for insurance claim count data
Modeling bivariate (or multivariate) count data has received increased interest in recent years. The aim is to model the number of different but correlated counts taking into account covariate information. Bivariate Poisson regression models based on the shock model approach are widely used because...
| Autores: | , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2445/191293 |
| Acceso en línea: | https://hdl.handle.net/2445/191293 |
| Access Level: | acceso abierto |
| Palabra clave: | Anàlisi de regressió Assegurances Mostreig (Estadística) Regression analysis Insurance Sampling (Statistics) |
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Copula-based bivariate finite mixture regression models with an application for insurance claim count dataBermúdez, LluísKarlis, DimitrisAnàlisi de regressióAssegurancesMostreig (Estadística)Regression analysisInsuranceSampling (Statistics)Modeling bivariate (or multivariate) count data has received increased interest in recent years. The aim is to model the number of different but correlated counts taking into account covariate information. Bivariate Poisson regression models based on the shock model approach are widely used because of their simple form and interpretation. However, these models do not allow for overdispersion or negative correlation, and thus, other models have been proposed in the literature to avoid these limitations. The present paper proposes copula-based bivariate finite mixture of regression models. These models offer some advantages since they have all the benefits of a finite mixture, allowing for unobserved heterogeneity and clustering effects, while the copula-based derivation can produce more flexible structures, including negative correlations and regressors. In this paper, the new approach is defined, estimation through an EM algorithm is presented, and then different models are applied to a Spanish insurance claim count databaseSpringer Verlag202220222022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion18 p.application/pdfhttps://hdl.handle.net/2445/191293Articles publicats en revistes (Matemàtica Econòmica, Financera i Actuarial)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésReproducció del document publicat a: https://doi.org/10.1007/s11749-022-00814-1TEST, 2022, vol. 31, p. 1082-1099https://doi.org/10.1007/s11749-022-00814-1cc by (c) Bermúdez et al., 2022http://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:recercat.cat:2445/1912932026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
Copula-based bivariate finite mixture regression models with an application for insurance claim count data |
| title |
Copula-based bivariate finite mixture regression models with an application for insurance claim count data |
| spellingShingle |
Copula-based bivariate finite mixture regression models with an application for insurance claim count data Bermúdez, Lluís Anàlisi de regressió Assegurances Mostreig (Estadística) Regression analysis Insurance Sampling (Statistics) |
| title_short |
Copula-based bivariate finite mixture regression models with an application for insurance claim count data |
| title_full |
Copula-based bivariate finite mixture regression models with an application for insurance claim count data |
| title_fullStr |
Copula-based bivariate finite mixture regression models with an application for insurance claim count data |
| title_full_unstemmed |
Copula-based bivariate finite mixture regression models with an application for insurance claim count data |
| title_sort |
Copula-based bivariate finite mixture regression models with an application for insurance claim count data |
| dc.creator.none.fl_str_mv |
Bermúdez, Lluís Karlis, Dimitris |
| author |
Bermúdez, Lluís |
| author_facet |
Bermúdez, Lluís Karlis, Dimitris |
| author_role |
author |
| author2 |
Karlis, Dimitris |
| author2_role |
author |
| dc.subject.none.fl_str_mv |
Anàlisi de regressió Assegurances Mostreig (Estadística) Regression analysis Insurance Sampling (Statistics) |
| topic |
Anàlisi de regressió Assegurances Mostreig (Estadística) Regression analysis Insurance Sampling (Statistics) |
| description |
Modeling bivariate (or multivariate) count data has received increased interest in recent years. The aim is to model the number of different but correlated counts taking into account covariate information. Bivariate Poisson regression models based on the shock model approach are widely used because of their simple form and interpretation. However, these models do not allow for overdispersion or negative correlation, and thus, other models have been proposed in the literature to avoid these limitations. The present paper proposes copula-based bivariate finite mixture of regression models. These models offer some advantages since they have all the benefits of a finite mixture, allowing for unobserved heterogeneity and clustering effects, while the copula-based derivation can produce more flexible structures, including negative correlations and regressors. In this paper, the new approach is defined, estimation through an EM algorithm is presented, and then different models are applied to a Spanish insurance claim count database |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022 2022 2022 |
| 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/2445/191293 |
| url |
https://hdl.handle.net/2445/191293 |
| 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/10.1007/s11749-022-00814-1 TEST, 2022, vol. 31, p. 1082-1099 https://doi.org/10.1007/s11749-022-00814-1 |
| dc.rights.none.fl_str_mv |
cc by (c) Bermúdez et al., 2022 http://creativecommons.org/licenses/by/3.0/es/ info:eu-repo/semantics/openAccess |
| rights_invalid_str_mv |
cc by (c) Bermúdez et al., 2022 http://creativecommons.org/licenses/by/3.0/es/ |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
18 p. application/pdf |
| dc.publisher.none.fl_str_mv |
Springer Verlag |
| publisher.none.fl_str_mv |
Springer Verlag |
| dc.source.none.fl_str_mv |
Articles publicats en revistes (Matemàtica Econòmica, Financera i Actuarial) reponame:Recercat. Dipósit de la Recerca de Catalunya instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Recercat. Dipósit de la Recerca de Catalunya |
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Recercat. Dipósit de la Recerca de Catalunya |
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