BAYESIAN ESTIMATION FOR THE STABLE DISTRIBUTIONS IN THE PRESENCE OF COVARIATES WITH APPLICATIONS IN CLINICAL ISSUES

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorFederal Technological University of Paraná-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorAchcar, Jorge Alberto-
Autor(es): dc.creatorSouza, Roberto Molina de-
Autor(es): dc.creatorBussola, Daiane-
Autor(es): dc.creatorMoala, Fernando A.-
Data de aceite: dc.date.accessioned2025-08-21T22:50:17Z-
Data de disponibilização: dc.date.available2025-08-21T22:50:17Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2021-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1590/0101-7438.2022.042.00254533-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/240406-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/240406-
Descrição: dc.descriptionIn this paper we explore a Bayesian approach for stable distributions in presence of covari-ates. This class of distribution has great flexibility for fitting asymmetric and heavy-tailed empirical data. These models are commonly used for data sets in finance and insurance. In this paper we show that these distributions can also be used to fit clinical data. Since there is not an analytical form for the density probability function which implies in serious difficulties to obtain the maximum likelihood estimators for the parameters, we use Bayesian methods with data augmentation techniques to get the inferences of interest. In this study we also discuss the choice of different prior distributions for the parameters considering regression models for the location and scale parameters of the stable distribution. We use MCMC (Markov Chain Monte Carlo) algorithms to generate samples from the posterior distributions in order to evaluate the point and interval estimators. A great simplification is obtained using the OpenBugs software. Two real data examples illustrate the applicability of the proposed modeling approach.-
Descrição: dc.descriptionUniversity of São Paulo Department of Public Health, Av. Bandeirantes, 3900, Monte Alegre,SP-
Descrição: dc.descriptionFederal Technological University of Paraná Department of Mathematics, Av. Alberto Carazzai, 1640, Centro,PR-
Descrição: dc.descriptionSão Paulo State University Department of Statistics, R. Roberto Símonsen, 305, Centro Educacional,SP-
Descrição: dc.descriptionSão Paulo State University Department of Statistics, R. Roberto Símonsen, 305, Centro Educacional,SP-
Idioma: dc.languageen-
Relação: dc.relationPesquisa Operacional-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectBayesian approach-
Palavras-chave: dc.subjectMCMC methods-
Palavras-chave: dc.subjectprior distributions-
Palavras-chave: dc.subjectregression models-
Palavras-chave: dc.subjecttable distributions-
Título: dc.titleBAYESIAN ESTIMATION FOR THE STABLE DISTRIBUTIONS IN THE PRESENCE OF COVARIATES WITH APPLICATIONS IN CLINICAL ISSUES-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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