Performance of a New Restricted Biased Estimator in Logistic Regression

Authors : Yasin ASAR

Pages : 53-59

Doi:10.19113/sdufbed.71595

View : 16 | Download : 17

Publication Date : 2018-04-16

Article Type : Research Paper

Abstract :It is known that the variance of the maximum likelihood estimator insert ignore into journalissuearticles values(MLE); inflates when the explanatory variables are correlated. This situation is called the multicollinearity problem. As a result, the estimations of the model may not be trustful. Therefore, this paper introduces a new restricted estimator insert ignore into journalissuearticles values(RLTE); that may be applied to get rid of the multicollinearity when the parameters lie in some linear subspace  in logistic regression. The mean squared errors insert ignore into journalissuearticles values(MSE); and the matrix mean squared errors insert ignore into journalissuearticles values(MMSE); of the estimators considered in this paper are given. A Monte Carlo experiment is designed to evaluate the performances of the proposed estimator, the restricted MLE insert ignore into journalissuearticles values(RMLE);, MLE and Liu-type estimator insert ignore into journalissuearticles values(LTE);. The criterion of performance is chosen to be MSE. Moreover, a real data example is presented. According to the results, proposed estimator has better performance than MLE, RMLE and LTE.
Keywords : Estimation, Liu type estimator, MLE, MSE, Multicollinearity, Monte Carlo simulation