Robust Regression Methods / a Comparison Study (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
The ordinary least squares method (OLS) is one of the most common methods for estimatingthe coefficients of linear regression models. However, it is sensitive and not robust againstthe existence of outliers. Therefore, several robust estimation methods have been used andthen represented by M-estimation using different objective functions. In this paper, a numberof alternative robust methods have been suggested that represented by using Gastwirth’slocation estimator instead of the mean in OLS and instead of the median in different Mestimationmethods. In addition to repeating the Hubers' M-estimation method (first method)until converged results are reached. A Monte-Carlo simulation study was employed toevaluate the performance of different estimation methods depending on the MSE ofregression coefficients.