An amalgamation of crisp and fuzzy quantile regression model

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dc.contributor.authorMustafa, Saima
dc.contributor.authorBasharat, Hina
dc.contributor.authorAkgul, Ali
dc.contributor.authorShahzad, Mohsin
dc.contributor.authorSayed, Abdelhamied Farrag
dc.date.accessioned2024-12-24T19:30:03Z
dc.date.available2024-12-24T19:30:03Z
dc.date.issued2024
dc.departmentSiirt Üniversitesi
dc.description.abstractFuzzy set theory is the most powerful tool to describe the process of uncertainty which exist in real world and fuzzy regression is an important research topic which can be used for prediction by establishing the functional relationship between fuzzy variables. Quantile regression is also a significant statistical method for estimating and drawing inferences about conditional quantile functions. This study introduced the idea of quantile regression with respect to fuzzy. The ordinary fuzzy regression is based on least square method but here we have introduced the idea of weighted least absolute deviation method in fuzzy regression. We have considered two different cases for the illustration of our proposed technique, firstly when the input and output are taken as fuzzy and secondly, the input and output are taken as fuzzy but the parameters are crisp. The algorithm for each case is based on linear programming problem (LPP). The LPP is constructed for individual case and solved it by the method of Simplex procedure. The proposed work is then compared with the conventional fuzzy regression by using AIC criterion. Empirical study shows that the proposed technique works best in every situation where the fuzzy regression fails and also provide the results in depth.
dc.identifier.doi10.14744/sigma.2024.00001
dc.identifier.endpage10
dc.identifier.issn1304-7205
dc.identifier.issn1304-7191
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85187295368
dc.identifier.scopusqualityQ3
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.14744/sigma.2024.00001
dc.identifier.urihttps://hdl.handle.net/20.500.12604/7368
dc.identifier.volume42
dc.identifier.wosWOS:001315923600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherYildiz Technical Univ
dc.relation.ispartofSigma Journal of Engineering and Natural Sciences-Sigma Muhendislik Ve Fen Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241222
dc.subjectFuzzy Regression
dc.subjectQuantile Regression
dc.subjectFuzzy Data
dc.subjectLinear Programming
dc.subjectSimplex Procedure
dc.titleAn amalgamation of crisp and fuzzy quantile regression model
dc.typeArticle

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