Comparison of Different Parametric Methods in Handling Critical Multicollinearity: Monte Carlo Simulation Study

Maxwell, Obubu and Chukwudike, C. Nwokike and Chinedu, O. Virtus and Valentine, C. Okoye and Paul, Obite Chukwudi (2019) Comparison of Different Parametric Methods in Handling Critical Multicollinearity: Monte Carlo Simulation Study. Asian Journal of Probability and Statistics, 3 (2). pp. 1-16. ISSN 2582-0230

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Abstract

In regression analysis, it is relatively necessary to have a correlation between the response and explanatory variables, but having correlations amongst explanatory variables is something undesired. This paper focuses on five methodologies for handling critical multicollinearity, they include: Partial Least Square Regression (PLSR), Ridge Regression (RR), Ordinary Least Square Regression (OLS), Least Absolute Shrinkage and Selector Operator (LASSO) Regression, and the Principal Component Analysis (PCA). Monte Carlo Simulations comparing the methods was carried out with the sample size greater than or equal to the levels considered in most cases, the Average Mean Square Error (AMSE) and Akaike Information Criterion (AIC) values were computed. The result shows that PCR is the most superior and more efficient in handling critical multicollinearity problems, having the lowest AMSE and AIC values for all the sample sizes and different levels considered.

Item Type: Article
Subjects: STM Open Press > Mathematical Science
Depositing User: Unnamed user with email support@stmopenpress.com
Date Deposited: 18 Apr 2023 05:47
Last Modified: 05 Sep 2024 11:07
URI: http://journal.submissionpages.com/id/eprint/935

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