A fuzzy nonparametric regression model based on an extended center and range method.

When dealing with nonparametric regression problems, kernel-based methods are often employed. In this paper, we investigate nonparametric regression problems with fuzzy responses and exact predictors by implementing an extended center and range method within a three-stage procedure. For this purpose, we utilize the Nadaraya–Watson estimator for estimating the unknown fuzzy smooth function. In each stage, the unknown bandwidth is determined with the help of a generalized cross validation criterion. To evaluate and to compare the performance of our proposed fuzzy nonparametric regression model with established regression models, different goodness-of-fit criteria are considered. Finally, we investigate the practical applicability and show the superiority of the proposed regression model by means of a simulation study and real data applications. • We develop a nonparametric regression model for fuzzy responses and exact predictors. • The nonlinear fuzzy smooth function is estimated within a three-stage procedure. • In each stage, the unknown bandwidth is evaluated by a generalized cross validation criterion. • We investigate different kernel functions and examine the model performance by various goodness-of-fit criteria. • We perform comparative analysis through simulation and real-life applications. [ABSTRACT FROM AUTHOR]