On choosing estimators'in a simple linear errors-in-variables model

This paper focuses on studying the accuracy of two well-known estimators in a simple errors-in-variables model, the ordinary least squares and the corrected least squares estimator. As a measure of accuracy of the estimators, the mean squared error is adopted. While Ketellapper (1983) addressed this issue for the case where the error of measurement in the independent variable is known, the present article is concerned with this comparison for the case where the ratio of the error variances is known. Comparison of the mean squared errors of the above estimators leads to a simple rule involving quantities estimable from the data, which can be used for deciding which of the two to be preferred on the basis of higher accuracy.