Improving the Performance of Rank Regression Using Fast Minimum Covariance Determinant in Estimating Weibull Distribution Parameters - Abduljabar Ibrahim Hasawy, Mohammed; Hussein Ali , Taha; Samad Sedeeq , Bekhal (International Journal of Advanced Science Computing and Engineering, 2025)
Outliers hurt the accuracy of life distribution parameters, including the Weibull distribution. Therefore, researchers have suggested employing the fast minimum covariance determinant method in rank regression estimators (which are robust but not efficient) to obtain robust estimators for the shape and scale parameters of the Weibull distribution. The proposed method is based on the robust means vector and the robust covariance matrix obtained from the fast minimum covariance determinant method, and it employs the rank regression estimation method, which depends on the ordinary least squares estimators of the simple linear regression model. The estimated parameters of the Weibull distribution obtained using the proposed technique have been compared with those derived from conventional maximum likelihood estimation and rank regression, using mean square error as the comparison metric, via both simulation and real data. The study's findings demonstrated the efficacy of the proposed strategy in addressing outliers and yielding highly effective estimators for the shape and scale parameters of the Weibull distribution.