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Research Article Open access CC BY 4.0

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

Obubu Maxwell, C. Nwokike Chukwudike, O. Virtus Chinedu, C. Okoye Valentine, Obite Chukwudi Paul

Asian Journal of Probability and Statistics · pp. 1–16 · Published 11 Mar 2019

10.9734/ajpas/2019/v3i230085

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.

Multicollinearity least absolute shrinkage and selection operator Partial Least Square Regression Akaike Information Criterion Average Mean Square Error Principal Component Analysis Ordinary Least Square Regression Ridge Regression

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