A fuzzy multiple regression model adopted with locally weighted and interval-valued techniques
In this study, a new method for fuzzy linear regression analysis characterized by crisp predictors and fuzzy responses is proposed. The fuzzy responses are decomposed into two separate closed intervals, and then a fuzzy linear regression model is fitted by using the mid-points and ranges of the inte...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
1 January 2026
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| In: |
Journal of computational and applied mathematics
Year: 2026, Jahrgang: 471, Pages: 1-11 |
| ISSN: | 1879-1778 |
| DOI: | 10.1016/j.cam.2025.116751 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.cam.2025.116751 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S0377042725002651 
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| Verfasserangaben: | Gholamreza Hesamian, Arne Johannssen, Nataliya Chukhrova |
| Zusammenfassung: | In this study, a new method for fuzzy linear regression analysis characterized by crisp predictors and fuzzy responses is proposed. The fuzzy responses are decomposed into two separate closed intervals, and then a fuzzy linear regression model is fitted by using the mid-points and ranges of the interval values that result from the center and bounds of the fuzzy responses. The coefficients of the model are estimated within a three-steps procedure by means of the locally weighted estimation procedure. In each step, the unknown bandwidth for identifying the neighboring data points is specified by means of cross-validation. The fuzzy predicted values are then determined via the mid-points and ranges of the predicted interval values. As for performance assessment and comparison with other fuzzy regression models, two approved goodness-of-fit measures are computed. The practical applicability of the proposed model is investigated in the context of a simulation study and four real-data applications. The empirical results reveal the superiority of the introduced regression model compared to its competitors. |
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| Beschreibung: | Online verfügbar: 21. Mai 2025, Artikelversion: 24. Mai 2025 Gesehen am 26.08.2025 |
| Beschreibung: | Online Resource |
| ISSN: | 1879-1778 |
| DOI: | 10.1016/j.cam.2025.116751 |