Improved Estimation of Regression Parameters in Measurement Error Models

Abstract

This paper deals with improved estimation of the regrssion parameters in measurement error models when the reliability ratio matrix is known. We consider empirical Bayes estimators which include (i) the unrestricted unbiased (UE) estimator, (ii) the preliminary test estimator (PTE), (iii) the James-Stein estimator (SE), and (iv) the Positive-Rule Stein estimator (PRSE). The biases and the risks under the squared loss function are evaluated for all the four estimaton; and compared.