A Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem

dc.creatorCASANOVA, Maria Paz
dc.creatorIGLESIAS, Pilar
dc.creatorBOLFARINE, Heleno
dc.date.accessioned2012-10-20T04:44:30Z
dc.date.accessioned2018-07-04T15:46:11Z
dc.date.available2012-10-20T04:44:30Z
dc.date.available2018-07-04T15:46:11Z
dc.date.created2012-10-20T04:44:30Z
dc.date.issued2010
dc.identifierCOMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.39, n.2, p.347-360, 2010
dc.identifier0361-0918
dc.identifierhttp://producao.usp.br/handle/BDPI/30479
dc.identifier10.1080/03610910903453427
dc.identifierhttp://dx.doi.org/10.1080/03610910903453427
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1627118
dc.description.abstractIn this article, we introduce a semi-parametric Bayesian approach based on Dirichlet process priors for the discrete calibration problem in binomial regression models. An interesting topic is the dosimetry problem related to the dose-response model. A hierarchical formulation is provided so that a Markov chain Monte Carlo approach is developed. The methodology is applied to simulated and real data.
dc.languageeng
dc.publisherTAYLOR & FRANCIS INC
dc.relationCommunications in Statistics-simulation and Computation
dc.rightsCopyright TAYLOR & FRANCIS INC
dc.rightsrestrictedAccess
dc.subjectBinary regression
dc.subjectDirichlet process
dc.subjectDosimetry problem
dc.subjectMCMC
dc.subjectPosterior distribution
dc.titleA Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem
dc.typeArtículos de revistas


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