Scale-mixture birnbaum-saunders quantile regression models applied to personal accident insurance data

Silva, Alan da; Santos, Helton Saulo Bezerra dos; Vila, Roberto; Pal, Suvra

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Campo DC	Valor	Idioma
dc.contributor.author	Silva, Alan da	pt_BR
dc.contributor.author	Santos, Helton Saulo Bezerra dos	pt_BR
dc.contributor.author	Vila, Roberto	pt_BR
dc.contributor.author	Pal, Suvra	pt_BR
dc.date.accessioned	2026-07-20T13:57:35Z	-
dc.date.available	2026-07-20T13:57:35Z	-
dc.date.issued	2025-3	pt_BR
dc.identifier.citation	DASILVA, Alan; SAULO, Helton; VILA, Roberto; PAL, Suvra. Scale-mixture birnbaum-saunders quantile regression models applied to personal accident insurance data. Computational and Applied Mathematics, [S. l.], v. 44, n. 2, 80, 2025. DOI: https://doi.org/10.1007/s40314-024-03037-2. Disponível em: https://link.springer.com/article/10.1007/s40314-024-03037-2.	pt_BR
dc.identifier.uri	https://doi.org/10.1007/s40314-024-03037-2	pt_BR
dc.identifier.uri	http://repositorio.unb.br/handle/10482/55454	-
dc.language.iso	eng	-
dc.publisher	Springer Science and Business Media LLC	pt_BR
dc.rights	Acesso Restrito	pt_BR
dc.title	Scale-mixture birnbaum-saunders quantile regression models applied to personal accident insurance data	pt_BR
dc.type	Artigo	pt_BR
dc.subject.keyword	Applications of Mathematics	pt_BR
dc.subject.keyword	Computational Mathematics and Numerical Analysis	pt_BR
dc.subject.keyword	EM algorithm	pt_BR
dc.subject.keyword	Hypothesis tests	pt_BR
dc.subject.keyword	Mathematical Applications in Computer Science	pt_BR
dc.subject.keyword	Mathematical Applications in the Physical Sciences	pt_BR
dc.subject.keyword	Monte Carlo simulation	pt_BR
dc.subject.keyword	Quantile regression	pt_BR
dc.subject.keyword	Scale-mixture Birnbaum-Saunders distribution	pt_BR
dc.identifier.doi	https://doi.org/10.1007/s40314-024-03037-2	pt_BR
dc.relation.publisherversion	https://link.springer.com/article/10.1007/s40314-024-03037-2	-
dc.description.abstract1	The modeling of personal accident insurance data has been a topic of high relevance in the insurance literature. This type of data often exhibits positive skewness and heavy tails. In this work, we propose a new quantile regression model based on the scale-mixture Birnbaum-Saunders distribution for modeling personal accident insurance data. The maximum likelihood estimates of the model parameters are obtained via the EM algorithm. Two Monte Carlo simulation studies are performed using the R software. The first study aims to analyze the performances of the EM algorithm to obtain the maximum likelihood estimates, and the randomized quantile and generalized Cox-Snell residuals. In the second simulation study, the size and power of the Wald, likelihood ratio, score and gradient tests are evaluated. The two simulation studies are conducted considering different quantiles of interest and sample sizes. Finally, a real insurance data set is analyzed to illustrate the proposed approach.	-
dc.identifier.orcid	https://orcid.org/0000-0002-4467-8652	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0003-1073-0114	pt_BR
dc.contributor.affiliation	University of Sao Paulo, Institute of Mathematics and Statistics	-
dc.contributor.affiliation	University of Brasilia, Department of Statistics	-
dc.contributor.affiliation	University of Texas at Arlington, Department of Mathematics	-
dc.contributor.affiliation	University of Brasilia, Department of Statistics	-
dc.contributor.affiliation	McMaster University, Department of Mathematics and Statistics, Hamilton, Ontario, Canada	-
dc.contributor.affiliation	University of Texas at Arlington, Department of Mathematics	-
dc.description.unidade	Instituto de Exatas (IE)	-
dc.description.unidade	Departamento de Estatística (IE EST)	-
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