On the stochastic restricted Liu-type maximum likelihood estimator in logistic regression

TL;DR

This paper introduces a new stochastic restricted Liu-type maximum likelihood estimator for logistic regression to address multicollinearity, providing theoretical properties, a parameter selection method, and simulation results.

Contribution

It proposes a novel estimator combining Liu-type and stochastic restrictions for logistic regression, with analysis and a practical biasing parameter selection method.

Findings

The new estimator effectively reduces multicollinearity effects.

Simulation results demonstrate improved estimation accuracy.

The paper provides guidelines for choosing the biasing parameter.

Abstract

In order to overcome multicollinearity, we propose a stochastic restricted Liu-type max- imum likelihood estimator by incorporating Liu-type maximum likelihood estimator (Inan and Erdo- gan, 2013) to the logistic regression model when the linear restrictions are stochastic. We also discuss the properties of the new estimator. Moreover, we give a method to choose the biasing parameter in the new estimator. Finally, a simulation study is given to show the performance of the new estimator.