Fuzzy robust regression analysis for fuzzy input- output data

Authors

DOI:

https://doi.org/10.15587/2312-8372.2015.54189

Keywords:

robust regression, fuzzy regression, membership function

Abstract

In multiple regression analysis data analysis is very important. If data set has outliers, robust methods are used in parameter estimates. When input data are fuzzy and data set has outlier, the weight matrix is defined by the membership function of the residuals . In this study, multiple regression is suggested when the dependent and independent variables are triangular fuzzy numbers and parameters estimation are crisp numbers. The weighted fuzzy least squares are used with the weight matrix. Outliers influence the model by very small degree of membership, the degrees of membership of the other observation values are 1 or close to 1, and the effects of those on the estimation of the regression model are very important. The fuzzy robust regression method may be able to detect outliers automatically. Thus, possible negative effects of the outlier on the model may be minimized.

Author Biography

Вера Ильинична Грицюк, Kharkiv National University of Radioelectronics, Lenin avenue,14, Kharkiv, 61166

Candidate of Technical Sciences, Associate Professor

Department of design and operation of electronic devices

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Published

2015-11-26

How to Cite

Грицюк, В. И. (2015). Fuzzy robust regression analysis for fuzzy input- output data. Technology Audit and Production Reserves, 6(2(26), 4–8. https://doi.org/10.15587/2312-8372.2015.54189

Issue

Vol. 6 No. 2(26) (2015): Mathematical modeling. Information technologies

Section

Mathematical Modeling: Original Research

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Copyright (c) 2016 Вера Ильинична Грицюк

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