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

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

2009-03-18

arxiv.org/abs/0903.3146v1

Statistics

Methodology

13 pages. Submitted to Statistical Papers

Scientific paper

10.1007/s00362-009-0243-7

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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