Jackknife Kibria-Lukman M-Estimator: Simulation and Application

The ordinary least square (OLS) method is very efficient in estimating the regression parameters a linear model under classical assumptions. If contains outliers, performance of OLS estimator becomes imprecise. Multicollinearity another issue that can reduce estimator. This study proposed Robust Jackknife Kibria-Lukman (RJKL) based on M-estimator to deal with multicollinearity and outliers. We examine superiority over existing estimators using theoretical proofs Monte Carlo simulations. put test once more real-world data. observed performs better than estimators.