A New Robust Method for Estimating Linear Regression Model in the Presence of Outliers

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dc.contributor.authorAlanamu, T
dc.contributor.authorOyeyemi, G. M.
dc.date.accessioned2023-07-27T09:17:10Z
dc.date.available2023-07-27T09:17:10Z
dc.date.issued2018
dc.description.abstractOrdinary Least-Squares (OLS) estimators for a linear model are very sensitive to unusual values in the design space or outliers among response values. Even single atypical value may have a large effect on the parameter estimates. In this paper, we propose a new class of robust regression method for the classical linear regression model. The proposed method was developed using regularization methods that allow one to handle a variety of inferential problems where there are more covariates than cases. Specifically, each outlying point in the data is estimated using case-specific parameter. Penalized estimators are often suggested when the number of parameters in the model is more than the number of observed data points. In light of this, we propose the use of Ridge regression method for estimating the case-specific parameters. The proposed robust regression method was validated using Monte-Carlo datasets of varying proportion of outliers. Also, performance comparison was done for the proposed method with some existing robust methods. Assessment criteria results using breakdown point and efficiency revealed the supremacy of the proposed method over the existing methods 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/20.500.12484/11653
dc.language.isoenen_US
dc.publisherAkamai University, U.S.Aen_US
dc.relation.ispartofseries19(1);125–132
dc.subjectRobust regression, Case indicator, Ridge regression, Outliersen_US
dc.titleA New Robust Method for Estimating Linear Regression Model in the Presence of Outliersen_US
dc.typeArticleen_US

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