More on the restricted almost unbiased Liu-estimator in Logistic regression

TL;DR

This paper introduces a new stochastic restricted almost unbiased logistic Liu-estimator (SRAULLE) to improve logistic regression in the presence of multicollinearity, demonstrating its effectiveness through simulations and real data analysis.

Contribution

The paper proposes a novel estimator, SRAULLE, that incorporates stochastic linear restrictions to address multicollinearity in logistic regression models.

Findings

SRAULLE outperforms existing estimators in SMSE during simulations.

The estimator shows improved accuracy in real data application.

Simulation results validate the effectiveness of the proposed method.

Abstract

To address the problem of multicollinearity in the logistic regression model, in this paper we propose a new estimator called Stochastic restricted almost unbiased logistic Liu-estimator (SRAULLE) when the prior information is available in the form of stochastic linear restrictions. A Monte Carlo simulation study was carried out to compare the performance of the proposed estimator with some existing estimators in the scalar mean squared error (SMSE) sense. Finally, a real data example was given to appraise the performance of the estimators.