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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| Published in | arXiv.org |
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| Main Authors | , , |
| Format | Paper Journal Article |
| Language | English |
| Published |
Ithaca
Cornell University Library, arXiv.org
26.08.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2331-8422 |
| DOI | 10.48550/arxiv.0903.3146 |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | Lemonte, Artur J Bolfarine, Heleno Patriota, Alexandre G |
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| BackLink | https://doi.org/10.1007/s00362-009-0243-7$$DView published paper (Access to full text may be restricted) https://doi.org/10.48550/arXiv.0903.3146$$DView paper in arXiv |
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| DOI | 10.48550/arxiv.0903.3146 |
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| Snippet | This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in... This paper develops a bias correction scheme for a multivariate heteroskedastic errors-in-variables model. The applicability of this model is justified in... |
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| SubjectTerms | 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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| Title | Improved maximum likelihood estimators in a heteroskedastic errors-in-variables model |
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