A Simulation Study on Ridge Regression Estimators in the Presence of Outliers and Multicollinearity

A simulation study is used to examine the robustness of six estimators on a multiple linear regression model with combined problems of multicollinearity and non–normal errors. The performance of the six estimators, namely the Ordinary Least Squares (LS), Ridge Regression (RIDGE), Ridge Least Absolute Value (RLAV), Weighted Ridge (WRID), MM and a robust ridge regression estimator based on MM estimator (RMM) are compared. The RMM is a modification of the Ridge Regression (RIDGE) by incorporating robust MM estimator. The empirical evidence shows that RMM is the best among the six estimators for many combinations of disturbance distribution and degree of multicollinearity