A Simulation Research on a Biased Estimator in Logistic Regression Model

Abstract

In this article, a biased estimator is proposed to combat multi-collinearity in the logistic regression model. The proposed estimator is a general estimator which includes other biased estimators, such as the ridge estimator and the Liu estimator as special cases. Necessary and sufficient conditions for the superiority of the new biased estimator over the maximum likelihood estimator, the ridge estimator are obtained and some properties in the mean squared error sense are discussed. Furthermore, a Monte Carlo simulation study is given to illustrate some of the theoretical results.