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A Robust Ridge Regression Approach in the Presence of Both Multicollinearity and Outliers in the Data

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dc.ConferencecodeMalaysian Math Sci Soc
dc.ConferencedateSEP 27-29, 2017
dc.ConferencelocationUniv Malaysia Terengganu, Sch Informat & Appl Math, MALAYSIA
dc.Conferencename24th National Symposium on Mathematical Sciences (SKSM)
dc.contributor.authorShariff, NSMen_US
dc.contributor.authorFerdaos, NAen_US
dc.date.accessioned2024-05-29T02:49:42Z
dc.date.available2024-05-29T02:49:42Z
dc.date.issued2017
dc.description.abstractMulticollinearity often leads to inconsistent and unreliable parameter estimates in regression analysis. This situation will be more severe in the presence of outliers it will cause fatter tails in the error distributions than the normal distributions. The wellknown procedure that is robust to multicollinearity problem is the ridge regression method. This method however is expected to be affected by the presence of outliers due to some assumptions imposed in the modeling procedure. Thus, the robust version of existing ridge method with some modification in the inverse matrix and the estimated response value is introduced. The performance of the proposed method is discussed and comparisons are made with several existing estimators namely, Ordinary Least Squares (OLS), ridge regression and robust ridge regression based on GM-estimates. The finding of this study is able to produce reliable parameter estimates in the presence of both multicollinearity and outliers in the data.
dc.identifier.doi10.1063/1.4995936
dc.identifier.issn0094-243X
dc.identifier.scopusWOS:000410777800113
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/10912
dc.identifier.volume1870
dc.languageEnglish
dc.language.isoen_US
dc.publisherAMER INST PHYSICSen_US
dc.sourceWeb Of Science (ISI)
dc.sourcetitlePROCEEDINGS OF THE 24TH NATIONAL SYMPOSIUM ON MATHEMATICAL SCIENCES (SKSM24): MATHEMATICAL SCIENCES EXPLORATION FOR THE UNIVERSAL PRESERVATION
dc.titleA Robust Ridge Regression Approach in the Presence of Both Multicollinearity and Outliers in the Data
dc.typeProceedings Paperen_US
dspace.entity.typePublication

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