L1 Correlation-Based Penalty in High-Dimensional Quantile Regression

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dc.authoridYuzbasi, Bahadir/0000-0002-6196-3201
dc.authoridAsar, Yasin/0000-0003-1109-8456;
dc.authorwosidYuzbasi, Bahadir/F-6907-2013
dc.authorwosidAsar, Yasin/V-5701-2017
dc.authorwosidAhmed, Syed/GSN-7305-2022
dc.contributor.authorYuzbasi, Bahadir
dc.contributor.authorAhmed, S. Ejaz
dc.contributor.authorAsar, Yasin
dc.date.accessioned2024-08-04T20:45:50Z
dc.date.available2024-08-04T20:45:50Z
dc.date.issued2018
dc.departmentİnönü Üniversitesien_US
dc.description4th International Conference on Big Data and Information Analytics (BigDIA) -- DEC 17-19, 2018 -- HOUSTON, TXen_US
dc.description.abstractIn this study, we propose a new method called L1 norm correlation based estimation in quantile regression in high-dimensional sparse models where the number of explanatory variables is large, may be larger than the number of observations, however, only some small subset of the predictive variables are important in explaining the dependent variable. Therefore, the importance of new method is that it addresses both grouping affect and variable selection. Monte Carlo simulations confirm that the new method compares well to the other existing regularization methods.en_US
dc.description.sponsorshipIEEE,Gulf Coast Consortia,Univ Texas Hlth Sci Ctr Houston, Dept biostatistics & Data Sci, Sch Public Hlthen_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canadaen_US
dc.description.sponsorshipThe research of Professor S. Ejaz Ahmed was partially supported by the Natural Sciences and Engineering Research Council of Canada.en_US
dc.identifier.isbn978-1-5386-6888-7
dc.identifier.scopus2-s2.0-85062852607en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11616/98723
dc.identifier.wosWOS:000458645000002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof2018 4th International Conference on Big Data and Information Analytics (Bigdia)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectVariable Selectionen_US
dc.subjectLasso Regressionen_US
dc.subjectCorrelation Based Penaltyen_US
dc.subjectQuantile Regressionen_US
dc.titleL1 Correlation-Based Penalty in High-Dimensional Quantile Regressionen_US
dc.typeConference Objecten_US

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