Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model

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 w...

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Bibliographic Details
Published inarXiv.org
Main Authors Patriota, Alexandre G, Lemonte, Artur J, Bolfarine, Heleno
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 26.08.2015
Subjects
Analytical chemistry
Astrophysics
Bias
Computer simulation
Economic models
Epidemiology
Mathematical models
Maximum likelihood estimators
Monte Carlo simulation
Organic chemistry
Statistics - Methodology
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ISSN2331-8422
DOI10.48550/arxiv.0903.3146

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Summary: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.
Bibliography:SourceType-Working Papers-1
ObjectType-Working Paper/Pre-Print-1
content type line 50
ISSN:2331-8422
DOI:10.48550/arxiv.0903.3146