African Journal of Mathematics and Statistics Studies ISSN: 2689-5323 Volume 6, Issue 3, 2023 (pp. 59-69) 59 Article DOI: 10.52589/AJMSS-EC8EWXUL DOI URL: https://doi.org/10.52589/AJMSS-EC8EWXUL www.abjournals.org ABSTRACT: Linear regression has been one of the most important statistical data analysis tools. Multiple regression is a type of regression where the dependent variable shows a linear relationship with two or more independent variables. OLS estimate is extremely sensitive to unusual observations (outliers), with low breakdown point and low efficiency. This paper reviews and compares some of the existing robust methods (Least Absolute Deviation, Huber M-Estimator, Bisquare M-Estimator, MM Estimator, Least Median Square, Least Trimmed Square, S-Estimator); a simulation method is used to compare the selected existing methods. It was concluded based on the results that for y direction outlier, the best estimator in terms of high efficiency and breakdown point of at most 0.3 is MM; for x direction outlier, the best estimator in term breakdown point of at most 0.4 is S; for x, y direction outlier, the best estimator in terms of high efficiency and breakdown point of at most 0.2 is MM. KEYWORDS: Linear Regression, Breakdown Point, Robust Estimators, Outlier. REVIEW OF SOME ROBUST ESTIMATORS IN MULTIPLE LINEAR REGRESSIONS IN THE PRESENCE OF OUTLIER(s) Alanamu T. 1 , Oyeyemi G.M. 2 , Olaniran R.O. 3 and Adetunji K.O. 4 1 Department of Mathematics, Kwara State College of Education, Ilorin taoheedatalanamu@yahoo.com 2 Department of Statistics, University of Ilorin, Nigeria gmoyeyemi@gmail.com 3 Department of Staistics, University of Ilorin, Nigeria rid4stat@yahoo.com 4 Department of Mathematics, Kwara State College of Education, Ilorin kalmarx4@yahoo.com Cite this article: Alanamu T., Oyeyemi G.M., Olaniran R.O., Adetunji K.O. (2023), Review of Some Robust Estimators in Multiple Linear Regressions in the Presence of Outlier(s). African Journal of Mathematics and Statistics Studies 6(3), 59-69. DOI: 10.52589/AJMSS- EC8EWXUL Manuscript History Received: 14 April 2023 Accepted: 5 May 2023 Published: 26 June 2023 Copyright © 2023 The Author(s). This is an Open Access article distributed under the terms of Creative Commons Attribution- NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0), which permits anyone to share, use, reproduce and redistribute in any medium, provided the original author and source are credited.