Performances of Some Improved Estimators and their Robust Version with Outliers

Music City Center 

This paper introduces enhanced estimators designed to address the issue of multicollinearity in multiple linear regression models. In addition to multicollinearity, the presence of outliers also presents a challenge in multiple linear regression analysis. To tackle these issues, this paper proposes several improved estimators, along with their robust counterparts, and compares their performance. The evaluation of these estimators is based on both Monte Carlo simulations and real-life data, under various outlier scenarios, including no outliers, one outlier, and two outliers. The mean squared error (MSE) is used as the performance criterion. The simulation results show that, when no outliers are present, the improved estimators outperform most of their robust versions. However, in the presence of one or two outliers, all robust versions of the improved estimators perform better than the conventional improved estimators.

Linear Regression

Mean Square Error

M-estimator

Multicollinearity

outliers

Ridge Regression 

Biometrics Section