A Revisit to the Application of Weighted Mixed Regression Estimation in Linear Regression Models with Missing Data

This paper deals with the application of the weighted mixed
regression estimation of the coefficients in a linear model when
some values of some of the regressors are missing. Taking the
weight factor as an arbitrary scalar, the performance of weighted
mixed regression estimator in relation to the conventional least
squares and mixed regression estimators is analyzed and the choice
of scalar is discussed. Then taking the weight factor as a specific
matrix, a family of estimators is proposed and its performance
properties under the criteria of bias vector and mean squared error
matrix are analyzed.