DC FieldValueLanguage
dc.contributor.authorStanojević, Bogdanaen_US
dc.contributor.authorStanojević, Milanen_US
dc.date.accessioned2022-12-09T11:53:10Z-
dc.date.available2022-12-09T11:53:10Z-
dc.date.issued2022-
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/4926-
dc.description.abstractThe role of the regression analysis is crucial in many disciplines. Addressing the fuzzy quadratic least square regression for observed data modeled by fuzzy numbers, we aim to emphasize how a methodology that does not fully comply to the extension principle may fail to predict fuzzy valued numbers. We also propose a solution approach that functions in full accordance to the extension principle, thus overcoming the shortcomings arisen from the practice of splitting the optimization of a fuzzy number in independent optimizations of its components.en_US
dc.publisherElsevieren_US
dc.rightsAttribution-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectFuzzy regression | Fuzzy numbers | Extension principleen_US
dc.titleQuadratic least square regression in fuzzy environmenten_US
dc.typeConference Paperen_US
dc.relation.conference9th International Conference on Information Technology and Quantitative Managementen_US
dc.relation.publicationProcedia Computer Scienceen_US
dc.identifier.doi10.1016/j.procs.2022.11.190-
dc.identifier.scopus2-s2.0-85146117390-
dc.contributor.affiliationComputer Scienceen_US
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Artsen_US
dc.relation.firstpage391-
dc.relation.lastpage396-
dc.relation.volume214-
dc.description.rankM33-
item.cerifentitytypePublications-
item.openairetypeConference Paper-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
crisitem.author.orcid0000-0003-4524-5354-
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