REGULARIZATION TECHNIQUES IN MULTIPLE LINEAR REGRESSION IN THE PESENCE OF MULTICOLLINEARITY

Simple item page
dc.contributor.authorOyegoke, O. A.
dc.contributor.authorOyeyemi, G. M.
dc.contributor.authorAdeleke, M. O.
dc.contributor.authorKolawole, R. O.
dc.date.accessioned2023-07-27T09:12:16Z
dc.date.available2023-07-27T09:12:16Z
dc.date.issued2020
dc.description.abstractMulticollinearity has been a serious problem in regression analysis. Ordinary least square (OLS) regression may result in high variability in the estimates of the regression coefficients in the presence of multicollinearity. Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression (RR), and Partial Least Squares (PLS) methods are well established methods that reduce the variability of the estimates by shrinking the coefficients and at the same time produce interpretable models by shrinking some coefficients. The performances of LASSO, Ridge Regression, PLS and OLS estimators were evaluated using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in the presence of multicolinearity using Monte Carlo Simulation. The simulations were done for different sample sizes: n (10, 50, 100, 150) and levels of multicollinearity: Mild (0.1 – 0.3), Low (0.4 – 0.6) and High (0.7 - 0.9). OLS had poor parameters estimate and produced wrong inferences, LASSO estimator is the best, while PLS is most efficient when the number of variables is greater than sample size.en_US
dc.description.sponsorshipself-sponsoreden_US
dc.identifier.citationFULafia Journal of Science and Technologyen_US
dc.identifier.issn2449 - 0954
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/11645
dc.language.isoenen_US
dc.publisherFaculty of Physical Sciences, Federal University of Lafia, Nigeria.en_US
dc.relation.ispartofseries6(1);48 - 53
dc.subjectMulticollinearity, Least Absolute Shrinkage and Selection Operator, Ridge Regression, Partial Least Squares and Estimatorsen_US
dc.titleREGULARIZATION TECHNIQUES IN MULTIPLE LINEAR REGRESSION IN THE PESENCE OF MULTICOLLINEARITYen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Paper 66.pdf
Size:
385.48 KB
Format:
Adobe Portable Document Format
Description:
Main article
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections