Comparison of some Robust Estimators in Multiple Regression in the Presence of Outliers

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dc.contributor.authorOyeyemi, G. M.
dc.contributor.authorAji, D. A.
dc.contributor.authorIbraheem, B. A.
dc.contributor.authorKareem, A. O.
dc.date.accessioned2023-07-27T09:11:02Z
dc.date.available2023-07-27T09:11:02Z
dc.date.issued2021
dc.description.abstractOutlier results are one of the problems of Ordinary Least Squares (OLS) in regression analysis. Some estimators have been suggested as alternatives to the Ordinary Least Squares (OLS) estimator to improve the accuracy of the parameter estimates in the linear regression model in the presence of outliers. In this study, six robust estimators of handling the problem of outliers: Robust-M, Robust-MM; Robust-S; Least Trimmed Squares (LTS); Least Median Squares (LMS); and Least Absolute Deviation (LAD) were compared with OLS using Variance criterion. The multiple linear regression model considered, had 4 predictor variables (p = 5) and one dependent variable and there were four levels each of percentage of outliers (10%, 20%, 30%, 40%), variance of outliers (σ_outlier^2=1,50,100,200) and sample sizes (n = 20, 50, 100, 200) were considered through Monte Carlo experiments. The experiment was carried out 1000 times. The results showed that when the variance of outlier is 1, that is, the outliers and variables have standard normal distribution, OLS had the least variance at all sample sizes. But as the variance increases and at all sample sizes, the robust estimators outperformed the OLS. The robust MM had least variance more consistently as the sample size increases at all variance level of the outlier and also as the sample size increases. Therefore, the Robust MM Estimator performed more consistently than the other robust estimators considered.en_US
dc.description.sponsorshipSelf-sponsoreden_US
dc.identifier.citationPacific Journal of Science and Technologyen_US
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/11643
dc.language.isoenen_US
dc.publisherAkamai University, U.S.Aen_US
dc.relation.ispartofseries22(2);81 - 90
dc.subjectEfficiency, Outlier, Parameter, Rank, Robust estimatoren_US
dc.titleComparison of some Robust Estimators in Multiple Regression in the Presence of Outliersen_US
dc.typeArticleen_US

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