Performance Evaluation of the Robust Stein Estimator in the Presence of Multicollinearity and Outliers

Multicollinearity and outliers are common challenges in multiple linear regression, often adversely affecting the properties of least squares estimators. To address these issues, several robust estimators have been developed to handle multicollinearity and outliers individually or simultaneously. More recently, the robust Stein estimator (RSE) was introduced, which integrates shrinkage and robustness to effectively mitigate the impact of both multicollinearity and outliers. Despite its theoretical advantages, the finite-sample performance of this approach under multicollinearity and outliers remains underexplored. First, outliers in the y direction have been the main focus of earlier research on the RSE, not considering that leverage points could substantially impact regression results. Second, this study addresses the gap by considering outliers in the y direction and leverage points, providing a more thorough assessment of the RSE robustness. Finally, to extend the limited existing benchmark, we compare and evaluate the RSE performance with a wide range of robust and classical estimators. This extends existing benchmarking, which is limited in the current literature. Several Monte Carlo (MC) simulations were conducted, considering both normal and heavy-tailed error distributions, with sample sizes, multicollinearity levels, and outlier proportions varied. Performance was evaluated using bootstrap estimates of root mean squared error (RMSE) and bias. The MC simulation results indicated that the RSE outperformed other estimators under several scenarios where both multicollinearity and outliers are present. Finally, real data studies confirm the MC simulation results.