摘要：AbstractThe performance of ordinary least squares estimator (OLSE) when there is multicollinearity (MC) in a linear regression model becomes inefficient. The principal components regression and the modified ridge-type estimator have been proposed at a different time to handle the problem of MC. However, in this paper, we developed a new estimator by combining these two estimators and derived the necessary and sufficient condition for its superiority over other competing estimators. Furthermore, we establish the dominance of this new estimator over other estimators through a simulation study, and numerical example in terms of the estimated mean squared error.
