Evaluating the Efficiency of the Jackknife Kibria-Lukman M-Estimator: A Simulation-Based Comparative Analysis

Although linear regression is frequently used in predictive analysis, the Ordinary Least Squares (OLS) estimator's accuracy is decreased by multicollinearity and outliers. In order to offer a reliable substitute, this study suggests the Jackknife Kibria-Lukman (JKL) M-Estimator, which combines Ridge shrinkage, Jackknife resampling, and M-estimation. In extreme multicollinearity settings with outliers, the JKL M-Estimator reduced MSEs by up to 50% when compared to OLS and 30% when compared to Ridge using Monte Carlo simulations. Furthermore, across estimators, the JKL M-Estimator consistently offered the lowest variation. The JKL M-Estimator reduced the average coefficient variance by 44% when compared to OLS and 25% when compared to Ridge when used to real-world economic data, demonstrating improved resistance to outliers and multicollinearity. These findings confirm that the JKL M-Estimator is a very accurate and stable estimator for real-world regression situations that defy conventional wisdom.