Estimating the Optimal Tune Parameter for the Binary Weighted Function in a Robust Regression Model
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Abstract
Outliers affect the accuracy of estimating multiple linear regression model parameters and lead to unacceptably large residual values; therefore, robust estimator methods such as the binary square function must be used to obtain more accurate parameters and robustness versus outliers. The proposed method involves choosing the optimal tune parameter value that produces the minimum mean square error of the multiple linear regression parameters and treating outliers for every data set. Simulation and real data were used to compare the efficiency of the models estimated based on the classical robust method for binary square function and the algorithm proposed through a MATLAB program dedicated to this purpose. The research results showed that the proposed algorithm is effective in processing extreme values, estimating the most optimal tuning parameters and precisely estimating the multiple linear regression model coefficients.
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