Smooth quantile regression and distributed inference for non-randomly stored big data

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dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-7201-6963en_US
dc.authorid0000-0002-4099-1254en_US
dc.contributor.authorWang, Kangning
dc.contributor.authorJia, Jiaojiao
dc.contributor.authorPolat, Kemal
dc.contributor.authorSun, Xiaofei
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorAlenezi, Fayadh
dc.date.accessioned2023-08-28T13:20:48Z
dc.date.available2023-08-28T13:20:48Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThe research was supported by NNSF project of China (11901356) .en_US
dc.description.abstractIn recent years, many distributed algorithms towards big data quantile regression have been proposed. However, they all rely on the data are stored in random manner. This is seldom in practice, and the violation of this assumption can seriously degrade their performance. Moreover, the non-smooth quantile loss brings inconvenience in both computation and theory. To solve these issues, we first propose a convex and smooth quantile loss, which converges to the quantile loss uniformly. Then a novel pilot sample surrogate smooth quantile loss is constructed, which can realize communication-efficient distributed quantile regression, and overcomes the non-randomly distributed nature of big data. In theory, the estimation consistency and asymptotic normality of the resulting distributed estimator are established. The theoretical results guarantee that the new method is adaptive to the situation where the data are stored in any arbitrary way, and can work well just as all the data were pooled on a single machine. Numerical experiments on both synthetic and real data verify the good performance of the new method.en_US
dc.description.sponsorshipNNSF project of China; [11901356]en_US
dc.identifier.citationWang, K., Jia, J., Polat, K., Sun, X., Alhudhaif, A., & Alenezi, F. (2023). Smooth quantile regression and distributed inference for non-randomly stored big data. Expert Systems with Applications, 215, 119418.en_US
dc.identifier.doi10.1016/j.eswa.2022.119418
dc.identifier.endpage13en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85144014600en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2022.119418
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11605
dc.identifier.volume215en_US
dc.identifier.wosWOS:000906819900001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectQuantile Regressionen_US
dc.subjectBig Dataen_US
dc.subjectCommunication Efficiencyen_US
dc.subjectMedian Regressionen_US
dc.subjectSelectionen_US
dc.titleSmooth quantile regression and distributed inference for non-randomly stored big dataen_US
dc.typeArticleen_US

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