Combating Multicollinearity: A New Two-Parameter Approach

Authors : Janet Iyabo IDOWU, Olasunkanmi James OLADAPO, Abiola Timothy OWOLABİ, Kayode AYİNDE, Oyinlade AKİNMOJU

Pages : 90-116

Doi:10.51541/nicel.1084768

View : 58 | Download : 63

Publication Date : 2023-06-30

Article Type : Research Paper

Abstract :The ordinary least square insert ignore into journalissuearticles values(OLS); estimator is the Best Linear Unbiased Estimator insert ignore into journalissuearticles values(BLUE); when all linear regression model assumptions are valid. The OLS estimator, however, becomes inefficient in the presence of multicollinearity. Various one and two-parameter estimators have been proposed to circumvent the problem of multicollinearity. This paper presents a new twoparameter estimator called Liu-Kibria Lukman Estimator insert ignore into journalissuearticles values(LKL); estimator. The proposed estimator is compared theoretically and through Monte Carlo simulation with existing estimators such as the ordinary least square, ordinary ridge regression, Liu, Kibria-Lukman, and Modified Ridge estimators. The results show that the proposed estimator performs better than existing estimators considered in this study under some conditions, using the mean square error criterion. A real-life application to Portland cement and Longley datasets supported the theoretical and simulation results by giving the smallest mean square error compared to the existing estimators.
Keywords : LKL Tahmin Edicisi, EKK Tahmin Edicisi, Monte Carlo Simulasyon, Çoklu İç İlişki, Hata Kereler Ortalaması