On the biased Two-Parameter Estimator to Combat Multicollinearity in Linear Regression Model

Janet Iyabo Idowu; Olasunkanmi James Oladapo; Abiola Timothy Owolabi; Kayode Ayinde

doi:10.46481/asr.2022.1.3.57

Authors

Janet Iyabo Idowu
Department of Statistics, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
Olasunkanmi James Oladapo
Department of Statistics, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
Abiola Timothy Owolabi
[email protected]

Department of Statistics, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria
Kayode Ayinde
Department of Statistics, Federal University of Technology, Akure, Nigeria

Keywords:

Multicollinearity, Ordinary least-squares, Simulation, Biased two-parameter, Ridge regression estimator

Abstract

The most popularly used estimator to estimate the regression parameters in the linear regression model is the ordinary least-squares (OLS). The existence of multicollinearity in the model renders OLS inefficient. To overcome the multicollinearity problem, a new two-parameter estimator, a biased two-parameter (BTP), is proposed as an alternative to the OLS. Theoretical comparisons and simulation studies were carried out. The theoretical comparison and simulation studies show that the proposed estimator dominated some existing estimators using the mean square error (MSE) criterion. Furthermore, the real-life data bolster both the hypothetical and simulation results. The proposed estimator is preferred to OLS and other existing estimators when multicollinearity is present in the model.

Dimensions

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