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

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-...

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Bibliographic Details
Published in:	African Scientific Reports pp. 188 - 204
Main Authors:	Idowu, Janet Iyabo, Oladapo, Olasunkanmi James, Owolabi, Abiola Timothy, Ayinde, Kayode
Format:	Journal Article
Language:	English
Published:	Nigerian Society of Physical Sciences 29-12-2022
Subjects:	multicollinearity, ordinary least-squares, simulation, biased two-parameter, ridge regression estimator
Online Access:	Get full text
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Summary:	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.
ISSN:	2955-1625 2955-1617
DOI:	10.46481/asr.2022.1.3.57