Performance of Some Biasing Parameter Estimates for Liu Estimator in Linear Regression Model with Multicollinearity - Puttamat, Wirawan (Journal of Applied Statistics and Information Technology, 2023)
In multiple linear regression models, the multicollinearity occurs when the explanatory variables in a regression model are correlated. The multicollinearity effects on the ordinary least squares estimation method since the estimated regression coefficients become unstable and difficult to interpret in the presence of multicollinearity. In order to mitigate the problem of multicollinearity, Liu regression is widely used as a biased method of estimation with biasing parameter . The purpose of this research is to investigate the performance of some biasing parameter estimates in Liu regression in the presence of moderate to high correlation among the explanatory variables. The simulation study and application using real data have been performed to evaluate the performance of these biasing parameter estimation methods due to the mean squared errors (MSE). Based on the results from the simulation and application using real data, it can be concluded that the estimators D8, D9, D13 and D15 have a better performance for Liu regression.