Performance of Some Dawoud-Kibra Estimators for Logistic Regression Model: Application to Pena data set

Abstract

A logistic regression model's parameters are usually estimated using the maximum likelihood (ML) method. As a consequence of the problem of multicollinearity, unstable parameter estimates are obtained, and the mean square error (MSE) obtained cannot also be relied upon. There have been several biased estimators proposed to deal with multicollinearity, and the logistic Dawoud-Kibra (LDK) estimator is one of them, and research has shown that biasing parameters have an effect on MSE, too. Our study proposed seven LDK biasing estimators and all of them were subjected to Monte Carlo simulations, as well as using Pena data sets. According to the simulation study, LDK estimators outperform Logistic Ridge Regression (LRR) and ML methods. Furthermore, application to Pena real data set also align with the simulation results.