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An application of robust ridge regression model in the presence of outliers to real data problem

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dc.ConferencecodeUniv Malaysia Pahang, Fac Ind Sci & Technol, Inst Teknologi Sepuluh, Soc Ind & Appl Math, Malaysian Math Sci Soc, Malaysian Inst Stat
dc.ConferencedateAUG 08-10, 2017
dc.ConferencelocationKuantan, MALAYSIA
dc.Conferencename1st International Conference on Applied and Industrial Mathematics and Statistics (ICoAIMS)
dc.FundingDetailsUniversiti Sains Islam Malaysia,Islamic Science University Of Malaysia
dc.FundingDetailsThe authors are grateful for the financial support from (RAGS-FST-50214-55) from Universiti Sains Islam Malaysia.
dc.contributor.affiliationsFaculty of Science and Technology
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorShariff, NSMen_US
dc.contributor.authorFerdaos, NAen_US
dc.date.accessioned2024-05-29T03:27:00Z
dc.date.available2024-05-29T03:27:00Z
dc.date.issued2017
dc.description.abstractMulticollinearity and outliers are often leads to inconsistent and unreliable parameter estimates in regression analysis. The well-known procedure that is robust to multicollinearity problem is the ridge regression method. This method however is believed are affected by the presence of outlier. The combination of GM-estimation and ridge parameter that is robust towards both problems is on interest in this study. As such, both techniques are employed to investigate the relationship between stock market price and macroeconomic variables in Malaysia due to curiosity of involving the multicollinearity and outlier problem in the data set. There are four macroeconomic factors selected for this study which are Consumer Price Index (CPI), Gross Domestic Product (GDP), Base Lending Rate (BLR) and Money Supply (M1). The results demonstrate that the proposed procedure is able to produce reliable results towards the presence of multicollinearity and outliers in the real data.en_US
dc.description.natureFinal
dc.editorMuhammad N.
dc.identifier.ArtNo12150
dc.identifier.citationN S Md. Shariff and N A Ferdaos 2017 J. Phys.: Conf. Ser. 890 012150en_US
dc.identifier.doi10.1088/1742-6596/890/1/012150
dc.identifier.issn1742-6588
dc.identifier.issue1
dc.identifier.scopusWOS:000423857800150
dc.identifier.scopus2-s2.0-85030705250
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85030705250&doi=10.1088%2f1742-6596%2f890%2f1%2f012150&partnerID=40&md5=dc23fe20497b6def4c7b38ec9bed7af8
dc.identifier.urihttps://iopscience.iop.org/article/10.1088/1742-6596/890/1/012150
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/12177
dc.identifier.volume890
dc.languageEnglish
dc.language.isoen_USen_US
dc.publisherIOP PUBLISHING LTDen_US
dc.relation.ispartofJournal of Physics: Conference Seriesen_US
dc.sourceWeb Of Science (ISI)
dc.sourcetitle1ST INTERNATIONAL CONFERENCE ON APPLIED & INDUSTRIAL MATHEMATICS AND STATISTICS 2017 (ICOAIMS 2017)
dc.titleAn application of robust ridge regression model in the presence of outliers to real data problemen_US
dc.typeArticleen_US
dspace.entity.typePublication

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