Liu-Type logistic estimator under Stochastic Linear Restrictions

Abstract

Abstract:To conquer the multicollinearity problem in logistic regression, many alternative estimators have been proposed in the literature when some linear restrictions on the parameter space are available in addition to the sample model. In this paper, we propose a new two parameter Liu-type estimator called Stochastic Restricted Liu-Type Logistic Estimator (SRLTLE) by combining Liu-type estimator with the logistic model in the presence of stochastic linear restrictions. Further, a Monte Carlo simulation study is done to compare the performance of the proposed estimator with some existing estimators in the scalar mean squared error (SMSE) sense, and a numerical example is given to illustrate the theoretical results.

A new kind of stochastic restricted biased estimator for logistic regression model

“…where d is the Liu shrinkage parameter. Also, Varathan and Wijekoon [25] proposed the following Stochastic Restricted Liu-Type Logistic Estimator (SRLTLE):…”

“…Also, Varathan and Wijekoon [ 25 ] proposed the following Stochastic Restricted Liu-Type Logistic Estimator (SRLTLE): Following Hubert and Wijekoon [ 10 ], we proposed a new general biased estimator which will be called the stochastic restricted two-parameters maximum likelihood estimator (SRTPMLE) when the linear stochastic restrictions are available for the logistic regression model. The organization of the paper is as follows.…”