A Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem

dc.creatorPaz Casanova, Maria
dc.creatorIglesias, Pilar
dc.creatorBolfarine, Heleno
dc.date.accessioned2024-01-10T12:04:05Z
dc.date.available2024-01-10T12:04:05Z
dc.date.created2024-01-10T12:04:05Z
dc.date.issued2010
dc.identifier10.1080/03610910903453427
dc.identifier0361-0918
dc.identifierhttps://doi.org/10.1080/03610910903453427
dc.identifierhttps://repositorio.uc.cl/handle/11534/75677
dc.identifierWOS:000274979200010
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.languageen
dc.publisherTAYLOR & FRANCIS INC
dc.rightsacceso restringido
dc.subjectBinary regression
dc.subjectDirichlet process
dc.subjectDosimetry problem
dc.subjectMCMC
dc.subjectPosterior distribution
dc.subjectNONPARAMETRIC-INFERENCE
dc.subjectBINARY REGRESSION
dc.subjectRESPONSE DATA
dc.subjectMODELS
dc.subjectBIOASSAY
dc.subjectLINK
dc.subjectDOSIMETRY
dc.titleA Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem
dc.typeartículo


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