Please use this identifier to cite or link to this item: https://ah.nccu.edu.tw/handle/140.119/18852


Title: A New Approach to Fuzzy Regression Models with Application to Business Cycle Analysis
Authors: 吳柏林
Wu, Berlin
Tseng, Neng-Fang
Contributors: 應用數學系
Keywords: Fuzzy regression;Fuzzy parameter;Triangular membership function;h-cut;Methods of least square
Date: 2002-08
Issue Date: 2008-12-24 13:39:45 (UTC+8)
Abstract: Recently, fuzzy regression analysis has been largely applied in the modeling of economic or financial data. However, those data often exhibit certain kinds of linguistic terms, for instance: very good, a little reclining or stable, in the business cycle or the growth rate of GDP, etc. The goal of this paper is to construct a fuzzy regression model by fuzzy parameters estimation using the fuzzy samples. It deals with imprecise measurement of observed variables, fuzzy least square estimation and nonparametric 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.
Relation: Fuzzy Sets and System,130(1),33-42
國立政治大學九十學年度 學術研究成果國際化優等獎
Data Type: article
DOI 連結: http://dx.doi.org/10.1016/S0165-0114(01)00175-0
Appears in Collections:[應用數學系] 期刊論文

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