Heteroscedastic Nonlinear Regression Models
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 present a generalization of the Bayesian methodology introduced by Cepeda and Gamerman (2001) for modeling variance heterogeneity in normal regression models where we have orthogonality between mean and variance parameters to the general case considering both linear and highly nonlinear regression models. Under the Bayesian paradigm, we use MCMC methods to simulate samples for the joint posterior distribution. We illustrate this algorithm considering a simulated data set and also considering a real data set related to school attendance rate for children in Colombia. Finally, we present some extensions of the proposed MCMC algorithm. Research Division of the National University of Colombia (Universidad Nacional de Colombia) Research Division of the National University of Colombia (Universidad Nacional de Colombia) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) CNPq-Brazil[300235/2005-4] |
Identificador |
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, NEW YORK, v.39, n.2, p.405-419, 2010 0361-0918 http://producao.usp.br/handle/BDPI/28767 10.1080/03610910903480784 |
Idioma(s) |
eng |
Publicador |
TAYLOR & FRANCIS INC NEW YORK |
Relação |
Communications in Statistics-simulation and Computation |
Direitos |
restrictedAccess Copyright TAYLOR & FRANCIS INC |
Palavras-Chave | #Bayesian analysis #Heteroscedasticity #MCMC algorithm #Nonlinear regression #Parameter estimation #Statistics & Probability |
Tipo |
article original article publishedVersion |