Modified New Estimators Depending on Unbiased Ridge Estimator For Linear Regression Model

Abstract

In this paper, we develop new three types of  biased estimators depending on the superiority of unbiased ridge regression estimator comparing to ordinary least square estimator when there is a multicollinearity among the explanatory variables. The bias, variance, mean square error matrix (MSE) and scalar mean square error (mse) of the proposed estimators are derived. The performance of these estimators are evaluated in comparison to that of other estimators by utilizing the MSE criterion. Finally, a numerical example is analyses in order to learn more about the performance of the new proposed estimators.