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1
Content available remote A method of variable selection for fuzzy regression - the possibility approach
100%
Gładysz B. , Kuchta D.
Operations Research and Decisions
|
2011
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tom Vol. 21, No. 2
5-15
EN
A method of variable selection for fuzzy regression has been proposed. Using the method, the significance of fuzzy regression coefficients has been examined. The method presented is equivalent to the method of variable selection for classical regression based on an analysis of the confidence intervals for their coefficients. Illustrative examples are presented.
2
Content available remote Outliers detection method in fuzzy regression
100%
Gładysz B.
Operations Research and Decisions
|
2010
|
tom Vol. 20, No. 2
26-39
EN
In this article we propose a method for identifying outliers in fuzzy regression. Outliers in a sample may have an important influence on the form of the regression equation. For this reason there is great scientific interest in this issue. The method presented is analogous to the method of finding outliers based on the studentized distribution of residuals. In order to identify outliers, regression models are constructed with an additional explanatory variable for each observation. Next, the significance of a fuzzy regression coefficient is analysed considering this additional explanatory variable. Illustrative examples are presented.
3
Content available remote A method of variable selection for fuzzy regression – the possibility approach
100%
GŁADYSZ B. , KUCHTA D.
Operations Research and Decisions
|
2011
|
tom 21
|
nr 2
5-15
EN
A method of variable selection for fuzzy regression has been proposed. Using the method, the significance of fuzzy regression coefficients has been examined. The method presented is equivalent to the method of variable selection for classical regression based on an analysis of the confidence intervals for their coefficients. Illustrative examples are presented.
4
Content available remote A method for detecting outliers in fuzzy regression
100%
GŁADYSZ B.
Operations Research and Decisions
|
2010
|
tom 20
|
nr 2
25-39
EN
In this article we propose a method for identifying outliers in fuzzy regression. Outliers in a sample may have an important influence on the form of the regression equation. For this reason there is great scientific interest in this issue. The method presented is analogous to the method of finding outliers based on the studentized distribution of residuals. In order to identify outliers, regression models are constructed with an additional explanatory variable for each observation. Next, the significance of a fuzzy regression coefficient is analysed considering this additional explanatory variable. Illustrative examples are presented.
5
Content available Fuzzy regression : a scalar variable formation
88%
Guo R. , Guo D.
Journal of Polish Safety and Reliability Association
|
2008
|
tom Vol. 1
159--167
EN
In reliability, quality control and risk analysis, fuzzy methodologies are more and more involved and inevitably introduced difficulties in seeking fuzzy functional relationship between factors. In this paper, we propose a scalar variable formation of fuzzy regression model based on the credibility measure theoretical foundation. It is expecting our scalar variable treatments on fuzzy regression models will greatly simplify the efforts to seeking fuzzy functional relationship between fuzzy factors. An M-estimator for the regression coefficients is obtained and accordingly the properties and the variance-covariance for the coefficient M-estimators are also investigated in terms of weighted least-squares arguments. Finally, we explore the asymptotic membership function for the coefficient M-estimators.
6
Content available remote Nephropathy forecasting in diabetic patients using a GA-based type-2 fuzzy regression model
63%
Shafaei Bajestani N. , Vahidian Kamyad A. , Nasli Esfahani E. , Zare A.
Biocybernetics and Biomedical Engineering
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2017
|
tom Vol. 37, no. 2
281--289
EN
Choosing a proper method to predict and timely prevent the complications of diabetes could be considered a significant step toward optimally controlling the disease. Since in medical research only small sample sizes of data are available and medical data always includes high levels of uncertainty and ambiguity, a type-2 fuzzy regression model seems to be an appropriate procedure for finding the relationship between outcome and explanatory variables in medical decision-making. In this paper, a new type-2 fuzzy regression model based on type-2 fuzzy time series concepts is used to forecast nephropathy in diabetic patients. Results in two examples show model efficiency. The use of such models in diabetes clinics is proposed.
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