Efficiency of the principal component Liu-type estimator in logistic regression model

TL;DR

This paper introduces a new principal component Liu-type estimator for logistic regression that addresses multicollinearity, demonstrating its advantages through theoretical analysis and simulation comparisons.

Contribution

It proposes a novel estimator combining principal component and Liu-type methods specifically for logistic regression to improve performance under multicollinearity.

Findings

The new estimator outperforms existing methods in mean squared error.

Simulation results confirm the estimator's effectiveness.

Theoretical analysis supports its superiority in asymptotic mean squared error.

Abstract

In this paper we propose a principal component Liu-type logistic estimator by combining the principal component logistic regression estimator and Liu-type logistic estimator to overcome the multicollinearity problem. The superiority of the new estimator over some related estimators are studied under the asymptotic mean squared error matrix. A Monte Carlo simulation experiment is designed to compare the performances of the estimators using mean squared error criterion. Finally, a conclusion section is presented.