On the restricted almost unbiased Liu estimator in the Logistic regression model

TL;DR

This paper introduces a new restricted almost unbiased Liu estimator for logistic regression models with multicollinearity and linear restrictions, analyzing its properties and demonstrating its effectiveness through simulations and real data.

Contribution

It proposes a novel estimator combining bias reduction and restrictions in logistic regression, extending previous work to improve estimation under multicollinearity.

Findings

The estimator has desirable statistical properties.

Simulation results show improved performance over existing methods.

Real data application confirms practical usefulness.

Abstract

It is known that when the multicollinearity exists in the logistic regression model, variance of maximum likelihood estimator is unstable. As a remedy, in the context of biased shrinkage ridge estimation, Chang (2015) introduced an almost unbiased Liu estimator in the logistic regression model. Making use of his approach, when some prior knowledge in the form of linear restrictions are also available, we introduce a restricted almost unbiased Liu estimator in the logistic regression model. Statistical properties of this newly defined estimator are derived and some comparison result are also provided in the form of theorems. A Monte Carlo simulation study along with a real data example are given to investigate the performance of this estimator.