Improved estimation under collinearity and squared error loss

Abstract

This paper examines the performance of several biased, Stein-like and empirical Bayes estimators for the general linear statistical model under conditions of collinearity. A new criterion for deleting principal components, based on an unbiased estimator of risk, is introduced. Using a squared error measure and Monte Carlo sampling experiments, the resulting estimator's performance is evaluated and compared with other traditional and non-traditional estimators.multicollinearity principal components linear regression Stein rules empirical Bayes estimators unbiased estimation of risk