Performance of New Liu-type Logistic Estimators in Combating Multicollinearity

Multicollinearity problem in logistic regression causes an inflation in the variance of the Maximum Likelihood (ML) estimator. To overcome this serious problem, some biased estimators such as: ridge estimator, Liu estimator and Liu-type estimator, were suggested as a way of having smaller Mean Squared Error (MSE) than ML estimator. This paper discusses these different biased estimators in the logistic regression and proposes some new ridge estimators by Mansson(2012) and Asar(2016) to be applied in the Liu-type estimators. A Monte Carlo simulation study was conducted to assess the performances of ridge and Liu-type estimators in the sense of MSE and Bias criteria. It was concluded that the new estimators perform well in the Liu-type estimation