Performance of some new Liu parameters for the linear regression model

Abstract

This article introduces some Liu parameters in the linear regression model based on the work of Shukur, Månsson, and Sjölander. These methods of estimating the Liu parameter d increase the efficiency of Liu estimator. The comparison of proposed Liu parameters and available methods has done using Monte Carlo simulation and a real data set where the mean squared error, mean absolute error and interval estimation are considered as performance criterions. The simulation study shows that under certain conditions the proposed Liu parameters perform quite well as compared to the ordinary least squares estimator and other existing Liu parameters.

Keywords:

• Multicollinearity
• Liu estimator
• Liu parameters
• mean squared error
• mean absolute error

Acknowledgments

The authors would like to thank the Editor and anonymous referee for their valuable comments and suggestions that definitely improved the quality of the article.