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Verfasst von:Hesamian, Gholamreza [VerfasserIn]   i
 Johannssen, Arne [VerfasserIn]   i
 Chukhrova, Nataliya [VerfasserIn]   i
Titel:A flexible soft nonlinear quantile-based regression model
Verf.angabe:Gholamreza Hesamian, Arne Johannssen, Nataliya Chukhrova
E-Jahr:2025
Jahr:March 2025
Umfang:25 S.
Illustrationen:Illustrationen
Fussnoten:Online veröffentlicht: 6. März 2025 ; Gesehen am 14.07.2025
Titel Quelle:Enthalten in: Fuzzy optimization and decision making
Ort Quelle:Dordrecht [u.a.] : Springer Science + Business Media B.V., 2002
Jahr Quelle:2025
Band/Heft Quelle:24(2025), 1, Seite 129-153
ISSN Quelle:1573-2908
Abstract:There are several models for soft regression analysis in the literature, but relatively few are based on quantiles, and these models are limited to the linear case. As quantile-based regression models offer a series of benefits (like robustness and handling of asymmetric distributions) but have not been considered in the nonlinear case, we present the first soft nonlinear quantile-based regression model in this paper. Considering nonlinearity instead of limiting to linearity in the modeling brings numerous advantages such as a higher flexibility, more accurate predictions, a better model fit and an improved explainability/interpretability of the model. In particular, we embed fuzzy quantiles into nonlinear regression analysis with crisp predictor variables and fuzzy responses. We propose a new method for parameter estimation by implementing a three-stage technique on the basis of the center and the spreads. In the framework of this procedure, we utilize kernel-fitting, a least quantile loss function, least absolute errors, and generalized cross-validation criteria to estimate the model parameters. We perform comprehensive comparative analysis with other soft nonlinear regression models that have demonstrated superiority in previous studies. The results reveal that the proposed nonlinear quantile-based regression technique leads to better outcomes compared to the competitors.
DOI:doi:10.1007/s10700-025-09441-5
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1007/s10700-025-09441-5
 DOI: https://doi.org/10.1007/s10700-025-09441-5
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Cross-validation
 Econometrics
 Explainability
 Financial Econometrics
 Fuzzy quantiles
 Fuzzy regression
 Kernel-fitting
 Least absolute errors
 Parametric Inference
 Quantitative Economics
 Quantitative Finance
 Robustness
 Statistical Finance
K10plus-PPN:1930443560
Verknüpfungen:→ Zeitschrift

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