A new approach to fuzzy regression models with application to business cycle analysis

Wu, Berlin; Tseng, Neng-Fang

doi:10.1016/s0165-0114(01)00175-0

Cited by 47 publications

(31 citation statements)

References 5 publications

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“…However, in fuzzy input data exist certain outliers, according to the linear programming estimation technique, the approach could obtain a huge but useless estimated fuzzy output. Therefore, a more precise explanation should be considered, which the H-level set of membership functions for representative input data should be closed estimated precisely H-level set of membership function (Wu & Tseng, 2002).…”

Section: A Fuzzy Support Vector Regression Modelmentioning

confidence: 99%

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A fuzzy support vector regression model for business cycle predictions

Lin

Pai

2010

Expert Systems with Applications

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Section: A Fuzzy Support Vector Regression Modelmentioning

confidence: 99%

“…Many investigations have been done in the analysis of business cycles (Banerji & Hiris, 2001;Layton, 1996Layton, , 1998Seip & McNown, 2007;Wu & Tseng, 2002;Yang & Kim, 2005). However, business cycles are often determined by a panel of macroeconomic experts, and thus, it is difficult to predict the index of business cycles.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy support vector regression model for business cycle predictions

Lin

Pai

2010

Expert Systems with Applications

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“…Wu and Cheng [31] identified a model structure through qualitative simulation; Casalino et al [4], Esogbue and Song [5], and Wu and Sun [32] discussed the concepts of fuzzy statistics and applied them to social surveys. Wu and Tseng [33] [12] proposed a new weight function of fuzzy numbers defined by the central point and radius. Moreover, Lin et al [11] proposed a method to recognize the underlying distribution function using its central point and radius, which gives us more information about the original fuzzy data.…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment of a Portfolio Selection Model Based on a Fuzzy Statistical Test

Lin

Watada

Wu³

2013

IEICE Trans. Inf. & Syst.

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SUMMARYThe objective of our research is to build a statistical test that can evaluate different risks of a portfolio selection model with fuzzy data. The central points and radiuses of fuzzy numbers are used to determine the portfolio selection model, and we statistically evaluate the best return by a fuzzy statistical test. Empirical studies are presented to illustrate the risk evaluation of the portfolio selection model with interval values. We conclude that the fuzzy statistical test enables us to evaluate a stable expected return and low risk investment with different choices for k, which indicates the risk level. The results of numerical examples show that our method is suitable for short-term investments.

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“…Many applications of fuzzy regression analysis can be found in various areas. Recently, for example, fuzzy regression models have been applied to actuarial analysis by Sanchez and Gomez [28] and business cycle analysis by Wu and Tseng [30]. Using fuzzy regression technique, Hong et al [16] have analyzed the problem of energy loss in electric distribution systems and Lee and Chen [25] have studied the problem of manpower 0020-0255/$ -see front matter Ó 2007 Elsevier Inc. All forecasting.…”

Section: Introductionmentioning

confidence: 99%