A fuzzy multiple regression model adopted with locally weighted and interval-valued techniques - Hesamian, Gholamreza; Johannssen, Arne; Chukhrova, Nataliya (Journal of computational and applied mathematics, 2026)
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.
Online verfügbar: 21. Mai 2025, Artikelversion: 24. Mai 2025
Gesehen am 26.08.2025