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

TL;DR

This paper introduces a bias correction method for multivariate heteroskedastic errors-in-variables models, improving estimation accuracy in fields with measurement errors and variable variances, validated through simulations and real data application.

Contribution

It proposes a novel bias correction scheme for heteroskedastic errors-in-variables models, enhancing estimator accuracy over existing methods.

Findings

Bias correction yields nearly unbiased estimates

Simulation results confirm improved estimator performance

Application to real data demonstrates practical utility

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.