A Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem
Contribuinte(s) |
UNIVERSIDADE DE SÃO PAULO |
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Data(s) |
20/10/2012
20/10/2012
2010
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Resumo |
In 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. |
Identificador |
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.39, n.2, p.347-360, 2010 0361-0918 http://producao.usp.br/handle/BDPI/30479 10.1080/03610910903453427 |
Idioma(s) |
eng |
Publicador |
TAYLOR & FRANCIS INC |
Relação |
Communications in Statistics-simulation and Computation |
Direitos |
restrictedAccess Copyright TAYLOR & FRANCIS INC |
Palavras-Chave | #Binary regression #Dirichlet process #Dosimetry problem #MCMC #Posterior distribution #NONPARAMETRIC-INFERENCE #BINARY REGRESSION #RESPONSE DATA #MODELS #BIOASSAY #LINK #DOSIMETRY #Statistics & Probability |
Tipo |
article original article publishedVersion |