Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model
Contribuinte(s) |
UNIVERSIDADE DE SÃO PAULO |
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Data(s) |
20/10/2012
20/10/2012
2011
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Resumo |
This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in areas such as astrophysics, epidemiology and analytical chemistry, where the variables are subject to measurement errors and the variances vary with the observations. We conduct Monte Carlo simulations to investigate the performance of the corrected estimators. The numerical results show that the bias correction scheme yields nearly unbiased estimates. We also give an application to a real data set. FAPESP (Fundacao de Amparo a Pesquisa do Estado de Sao Paulo, Brazil) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) CNPq (Conselho Nacional de Desenvolvimento Cientifico e Tecnologico, Brazil) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) |
Identificador |
STATISTICAL PAPERS, v.52, n.2, p.455-467, 2011 0932-5026 http://producao.usp.br/handle/BDPI/30435 10.1007/s00362-009-0243-7 |
Idioma(s) |
eng |
Publicador |
SPRINGER |
Relação |
Statistical Papers |
Direitos |
closedAccess Copyright SPRINGER |
Palavras-Chave | #Bias correction #Errors-in-variables model #Heteroskedastic model #Maximum-likelihood estimation #NONLINEAR-REGRESSION #BIAS CORRECTION #Statistics & Probability |
Tipo |
article original article publishedVersion |