A Bayesian Semiparametric Approach for Solving the Discrete Calibration Problem


Autoria(s): CASANOVA, Maria Paz; IGLESIAS, Pilar; BOLFARINE, Heleno
Contribuinte(s)

UNIVERSIDADE DE SÃO PAULO

Data(s)

20/10/2012

20/10/2012

2010

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

http://dx.doi.org/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