Abstract
The ordinary least-squares estimator is commonly used to estimate the parameters of a linear regression model but gives unreliable and unfavorable results when two problems occur together: multicollinearity and outliers. This article proposes two different robust estimators of the regression parameters to cope with these problems together. The proposed estimators are a robust version of the ozkale-Kaciranlar and Yang-Chang estimators. Theoretical calculations, numerical simulations, and real-life data on manufacturing production are presented to demonstrate the superiority of the proposed robust estimators to existing estimators at dealing with multicollinearity and outliers at the same time.