improvement of the mixed liu estimator applying jackknife method in linear regression models

In the presence of multicollinearity in the regression models‎, ‎the ordinary least squares estimator loses its performance‎. ‎some solutions to reduce the effects of multicollinearity have been proposed‎, ‎including the application of biased estimators such as liu estimate and estimation under linear restrictions‎. ‎but due to the liu estimator being biased‎, ‎the jackknife method has been introduced to reduce the bias‎. ‎in this paper‎, ‎we will examine the jackknifed liu estimator and propose a new estimator under stochastic linear restrictions namely stochastic restricted jackknifed liu estimator‎. ‎a simulation study is conducted to investigate the performance of this new estimator using two measures namely mean squared errors and absolute bias‎. ‎from simulation study results‎, ‎we find that the new estimator outperforms the ols and liu estimators‎, ‎and it is superior to both ols and liu estimators‎, ‎using the mean squared errors and absolute bias criteria‎.