An investigation into properties of jackknifed and bootstrapped liu-type estimator

In 2003, Liu [16] proposed a new estimator dealing with the problem of multicollinearity in linear regression model pointing out a drawback of ridge estimator used in this context. This new estimator, called Liutype estimator was demonstrated to have lesser mean squared error than ridge estimator and ordinary least squares estimator, however, it may carry a large amount of bias. In the present paper, we propose an estimator in order to reduce the bias of Liu-type estimator, using the jackknife technique. We also propose bootstrap method to correct the bias of the Liu-type estimator. The bias and mean squared error of these estimators have been compared using a simulation study.