Title:	Scale-mixture birnbaum-saunders quantile regression models applied to personal accident insurance data
Authors:	Silva, Alan da Santos, Helton Saulo Bezerra dos Vila, Roberto Pal, Suvra
metadata.dc.identifier.orcid:	https://orcid.org/0000-0002-4467-8652 https://orcid.org/0000-0003-1073-0114
metadata.dc.contributor.affiliation:	University of Sao Paulo, Institute of Mathematics and Statistics University of Brasilia, Department of Statistics University of Texas at Arlington, Department of Mathematics University of Brasilia, Department of Statistics McMaster University, Department of Mathematics and Statistics, Hamilton, Ontario, Canada University of Texas at Arlington, Department of Mathematics
Assunto::	Applications of Mathematics Computational Mathematics and Numerical Analysis EM algorithm Hypothesis tests Mathematical Applications in Computer Science Mathematical Applications in the Physical Sciences Monte Carlo simulation Quantile regression Scale-mixture Birnbaum-Saunders distribution
Issue Date:	Mar-2025
Publisher:	Springer Science and Business Media LLC
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.
Abstract:	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.
metadata.dc.description.unidade:	Instituto de Exatas (IE) Departamento de Estatística (IE EST)
DOI:	https://doi.org/10.1007/s40314-024-03037-2
metadata.dc.relation.publisherversion:	https://link.springer.com/article/10.1007/s40314-024-03037-2
Appears in Collections:	Artigos publicados em periódicos e afins

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