Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/81032
DC FieldValueLanguage
dc.contributor應數系-
dc.creatorWu, Berlin-
dc.creator吳柏林zh_TW
dc.creatorLiu, Hsuan-Kuen_US
dc.creatorHsu, Yu-Yunen_US
dc.date2010-03-
dc.date.accessioned2016-02-01T08:07:15Z-
dc.date.available2016-02-01T08:07:15Z-
dc.date.issued2016-02-01T08:07:15Z-
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/81032-
dc.description.abstractThe goal of this paper is to construct a multiple fuzzy regression model by fuzzy parameters estimation using the fuzzy samples. We propose an optimization model that provides fuzzy coefficients to minimize the distance between the fuzzy regressands and the fuzzy regressors. It concerned with imprecise measurement of observed variables, linear programming estimation and non-parametric methods. This is different from the assumptions as well as the estimation techniques of the classical analysis. Empirical results demonstrate that our new approach is efficient and more realistic than the traditional regression analysis did.-
dc.format.extent178038 bytes-
dc.format.mimetypeapplication/pdf-
dc.relationInternational Journal of Intelligent Technologies & Applied Statistics, 3(1), 45-55-
dc.subjectFuzzy regression;Fuzzy parameter;H-cut;Methods of least square;Triangular membership function-
dc.titleOn the Optimization Methods for Fully Fuzzy Regression Models-
dc.typearticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextrestricted-
item.openairetypearticle-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
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