A bayesian approach for modeling interval-valued variables

dc.contributor.authorMorales, Fidel Castro
dc.contributor.authorLima Neto, Eufrásio de Andrade
dc.date.accessioned2022-10-11T20:14:16Z
dc.date.available2022-10-11T20:14:16Z
dc.date.issued2014
dc.description.resumoThis paper proposes two Bayesian approaches to estimate the regression model coefficients considering interval-valued variables as response and explanatory variables. The first approach considers a more simple co-variance structure, while the second approach supposes a more general co-variance structure. The posterior distribution for the parameters was approximated considering Markov Chain Monte Carlo method (MCMC). A simulation study is presented and suggests the effectiveness of the sampling scheme in recovering the true values of the parameters and also indicates convergence of the parameter estimate algorithm. The new approaches are applied to real interval-valued data sets and their performance compared.pt_BR
dc.identifier.citationMORALES, Fidel Castro; LIMA NETO, Eufrásio de Andrade. A bayesian approach for modeling interval-valued varaiables. Revista Brasileira de Biometria, São Paulo, v. 32, p. 360, 2014. Disponível em:http://jaguar.fcav.unesp.br/RME/fasciculos/v32/v32_n3/indice_v32_n3.php. Acesso em: 06 dez. 2017pt_BR
dc.identifier.issn0102-0811
dc.identifier.urihttps://repositorio.ufrn.br/handle/123456789/49551
dc.languagept_BRpt_BR
dc.publisherRevista Brasileira de Biometriapt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectInterval variablespt_BR
dc.subjectRegressionpt_BR
dc.subjectMCMCpt_BR
dc.subjectBayesian approachpt_BR
dc.titleA bayesian approach for modeling interval-valued variablespt_BR
dc.typearticlept_BR

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