Why Kappa Regression?

Authors

Julio C. Urenda, The University of Texas at El PasoFollow
Orsolya Csiszár, University of Applied Sciences, Esslingen, GermanyFollow
József Dombi, University of SzegedFollow
György Eigner, Óbuda University, BudapestFollow
Olga Kosheleva, The University of Texas at El PasoFollow
Vladik Kreinovich, The The University of Texas at El PasoFollow

Publication Date

5-1-2021

Comments

Technical Report: UTEP-CS-21-26a

Published in Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP),Bratislava, Slovakia, September 19-24, 2021,, Atlantis Press, Dordrecht, the Netherlands, pp. 478-485.

Abstract

A recent book provide examples that a new class of probability distributions and membership functions -- called kappa-regression distributions and membership functions -- leads to better data processing results than using previously known classes. In this paper, we provide a theoretical explanation for this empirical success -- namely, we show that these distributions are the only ones that satisfy reasonable invariance requirements.