A ridge regression estimation approach to the measurement error model

Abstract

This paper considers the estimation of the parameters of measurement error models where the estimated covariance matrix of the regression parameters is ill conditioned. We consider the Hoerl and Kennard type (1970) ridge regression (RR) modifications of the five quasi-empirical Bayes estimators of the regression parameters of a measurement error model when it is suspected that the parameters may belong to a linear subspace. The modifications are based on the estimated covariance matrix of the estimators of regression parameters. The estimators are compared and the dominance conditions as well as the regions of optimality of the proposed estimators are determined based on quadratic risks.