Handling Multicollinearity and Outliers in Logistic Regression Using the Robust Kibria-Lukman Estimator

Logistic regression models encounter challenges with correlated predictors and influential outliers. This study integrates robust estimators, including the Bianco-Yohai estimator (BY) and conditionally unbiased bounded influence estimator (CE), with the logistic Liu (LL), logistic ridge (LR), and logistic KL (KL) estimators. The resulting estimators (LL-BY, LL-CE, LR-BY, LR-CE, KL-BY, and KL-CE) are evaluated through simulations and real-life examples. KL-BY emerges as the preferred choice, displaying superior performance by reducing mean squared error (MSE) values and exhibiting robustness against multicollinearity and outliers. Adopting KL-BY can lead to stable and accurate predictions in logistic regression analysis.