Modified One-Parameter Liu Estimator for the Linear Regression Model

• Published in: Modelling and simulation in engineering Vol. 2020; pp. 1 - 17
• Main Authors: Lukman, Adewale F., Kibria, B. M. Golam, Ayinde, Kayode, Jegede, Segun L.
• Format: Journal Article
• Language: English
• Published: New York Hindawi 2020; Hindawi Limited
• Subjects: Bias; Computer simulation; Estimators; Mean square errors; Parameter estimation; Parameter modification; Regression analysis; Regression models; Simulation
• ISSN: 1687-5591; 1687-5605
• DOI: 10.1155/2020/9574304

Motivated by the ridge regression (Hoerl and Kennard, 1970) and Liu (1993) estimators, this paper proposes a modified Liu estimator to solve the multicollinearity problem for the linear regression model. This modification places this estimator in the class of the ridge and Liu estimators with a single biasing parameter. Theoretical comparisons, real-life application, and simulation results show that it consistently dominates the usual Liu estimator. Under some conditions, it performs better than the ridge regression estimators in the smaller MSE sense. Two real-life data are analyzed to illustrate the findings of the paper and the performances of the estimators assessed by MSE and the mean squared prediction error. The application result agrees with the theoretical and simulation results.