Hybrid Fuzzy Regression Analysis Using the F-Transform

This paper proposes a hybrid estimation algorithm for independently estimating the response function for the center and the response function for the spread in fuzzy regression model. The proposed algorithm combines the least absolute deviations estimation with discriminant analysis. In addition, the F-transform is used to convert spreads of the dependent variable into several groups. Two examples show that our method is superior to the existing methods based on the fuzzy regression model that assumes the same function for spread and center.

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Quadrilateral Interval Type-2 Fuzzy Regression Analysis for Data Outlier Detection

Mathematical Problems in Engineering ◽

10.1155/2019/4914593 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Pingping Gao ◽

Yabin Gao

Keyword(s):

Regression Analysis ◽

Regression Model ◽

Outlier Detection ◽

Euclidean Distance ◽

Fuzzy Numbers ◽

Fuzzy Regression ◽

Fuzzy Regression Analysis ◽

Fuzzy Regression Model ◽

Interval Type

This paper presents a fuzzy regression analysis method based on a general quadrilateral interval type-2 fuzzy numbers, regarding the data outlier detection. The Euclidean distance for the general quadrilateral interval type-2 fuzzy numbers is provided. In the sense of Euclidean distance, some parameter estimation laws of the type-2 fuzzy linear regression model are designed. Then, the data outlier detection-oriented parameter estimation method is proposed using the data deletion-based type-2 fuzzy regression model. Moreover, based on the fuzzy regression model, by using the root mean squared error method, an impact evaluation rule is designed for detecting data outlier. An example is finally provided to validate the presented methods.

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Mathematical programming approach to formulate intuitionistic fuzzy regression model based on least absolute deviations

Fuzzy Optimization and Decision Making ◽

10.1007/s10700-020-09315-y ◽

2020 ◽

Vol 19 (2) ◽

pp. 191-210 ◽

Cited By ~ 2

Author(s):

Liang-Hsuan Chen ◽

Sheng-Hsing Nien

Keyword(s):

Mathematical Programming ◽

Regression Model ◽

Fuzzy Regression ◽

Programming Approach ◽

Least Absolute Deviations ◽

Model Based ◽

Intuitionistic Fuzzy ◽

Fuzzy Regression Model ◽

Mathematical Programming Approach

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Method of Constructing the Fuzzy Regression Model of Bank Competitiveness

Journal of Central Banking Theory and Practice ◽

10.2478/jcbtp-2018-0016 ◽

2018 ◽

Vol 7 (2) ◽

pp. 139-164 ◽

Cited By ~ 3

Author(s):

Liudmyla Malyaretz ◽

Oleksandr Dorokhov ◽

Liudmyla Dorokhova

Keyword(s):

Regression Analysis ◽

Commercial Bank ◽

Multivariate Regression ◽

Solution Process ◽

Multivariate Regression Analysis ◽

Fuzzy Regression ◽

Fuzzy Regression Analysis ◽

Literary Sources ◽

Fuzzy Regression Model ◽

Efficiency Indicators

Abstract The paper substantiates the need to consider economic efficiency indicators of bank activity as fuzzy quantities. Formulations of the problem of fuzzy regression analysis and modelling, available in literary sources, have been analyzed. Three main approaches to the fuzzy regression analysis are presented. The general mathematical and meaningful formulation of problem of a fuzzy multivariate regression analysis for commercial bank competitiveness has been proposed. Sequence of its solutions is described. The example of numerical computations for one of the large Ukrainian banks is given. Results of obtained solution were analyzed from the standpoint of reliability, accuracy and compared against the classical crisp regression analysis. Finishing steps for obtaining final accurate numerical results of solution process are described. In summary, convincing arguments concerning the expediency of application of this approach to the problem of determining the competitiveness of banks are formulated and presented.

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