Ridge Estimation’s E�ectiveness for Multiple Linear Regression
with Multicollinearity: An Investigation Using Monte-Carlo
Simulations

dc.creator	Obadina, O. G., Adedotun, Adedayo F., Odusanya, Oluwaseun A.
dc.date	2021
dc.date.accessioned	2025-04-15T11:13:12Z
dc.description	The goal of this research is to compare multiple linear regression coe�cient estimation technique with multicollinearity. In order to quantify the e�ectiveness of estimations by the mean of average mean square error, the ordinary least squares technique (OLS), modified ridge regression method (MRR), and generalized Liu-Kejian method (LKM) are compared with the Average Mean Square Error (AMSE). For this study, the simulation scenarios are 3 and 5 independent variables with zero mean normally distributed random error of variance 1, 5, and 10, three correlation coe�cient levels; i.e., low (0.2), medium (0.5), and high (0.8) are determined for independent variables, and all combinations are performed with sample sizes 15, 55, and 95 by Monte Carlo simulation technique for 1,000 times in total. As the sample size rises, the AMSE decreased. The MRR and LKM both outperformed the OLS. At random error of variance 10, the MRR is the most suitable for all circumstances
dc.format	application/pdf
dc.identifier	http://eprints.covenantuniversity.edu.ng/17577/
dc.identifier.uri	https://repository.covenantuniversity.edu.ng/handle/123456789/48297
dc.language	en
dc.subject	QA Mathematics
dc.title	Ridge Estimation’s E�ectiveness for Multiple Linear Regression with Multicollinearity: An Investigation Using Monte-Carlo Simulations
dc.type	Article

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