"Biasing Estimator to Mitigate Multicollinearity in Linear Regression M" by Abdulrasheed Bello Badawaire, Issam Dawoud et al.

Al-Bahir Journal for Engineering and Pure Sciences

Article Title

Biasing Estimator to Mitigate Multicollinearity in Linear Regression Model

Authors

Abdulrasheed Bello Badawaire, Department of Mathematics and Statistics, Federal University Wukari, Wukari, NigeriaFollow
Issam Dawoud, Department of Mathematics, Al-Aqsa University, Gaza, Palestine
Adewale Folaranmi Lukman, Department of Mathematics, University of Medical Scieces, Ondo, Nigeria
Victoria Laoye, Department of Statistics, University of Ibadan, Ibadan, Nigeria
Arowolo Olatunji, Department of Mathematical Sciences, Lagos State University of Science and Technology, Lagos, Nigeria.

Abstract

A new two-parameter estimator was developed to combat the threat of multicollinearity for the linear regression model. Some necessary and sufficient conditions for the dominance of the proposed estimator over ordinary least squares (OLS) estimator, ridge regression estimator, Liu estimator, KL estimator, and some two-parameter estimators are obtained in the matrix mean square error sense. Theory and simulation results show that, under some conditions, the proposed two-parameter estimator consistently dominates other estimators considered in this study. The real-life application result follows suit.

Recommended Citation

Badawaire, Abdulrasheed Bello; Dawoud, Issam; Lukman, Adewale Folaranmi; Laoye, Victoria; and Olatunji, Arowolo (2023) "Biasing Estimator to Mitigate Multicollinearity in Linear Regression Model," Al-Bahir Journal for Engineering and Pure Sciences: Vol. 2 : Iss. 1 , Article 1.
Available at: https://doi.org/10.55810/2312-5721.1011

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