Comparison of linear type ii regression tecniques via monte-carlo simulation study: robust, fuzzy and robust fuzzy techniques

The aim of this study is comparatively examine the OLS (Ordinary Least Squares) Bisector, Fuzzy OLS Bisector, Robust OLS and Fuzzy Robust OLS techniques via Monte-Carlo Simulation study, when both the dependent and independent variables includes measurement errors in a simple linear regression analysis.In conditions, whether the interested data sets includes or not any outliers, the performance of the regression techniques are examined for different sample sizes (n=10, 50 and 100) and different distribution types (t_((4)), t_((10)) and t_((30))) according to Mean Square Error (MSE) criteria. According to findings of the study, the Fuzzy Huber OLS Bisector technique has the lowest MSE in all different distribution types and for all sample sizes for the data sets that either includes or not any outliers.