Abstract
Statistical modeling is essential in revealing the relationships between variables. These models can be classified as parametric and nonparametric methods in studies using crisp values. However, most of the data collected are inherently fuzzy. In this framework, it has been a subject that has been studied from past to present that the methods derived for exact values are expressed as methods with fuzzy valued input and output variables. The study aims to describe nonparametric local polynomial regression models in fuzzy structure to examine the results for cases where an input variable is a crisp number, and the output variable is a symmetrical triangular and trapezoidal fuzzy number. According to the results, the bandwidth parameter was smaller in models where the degree of the polynomial was taken as one and larger in the case of three. In addition, the bandwidth parameter was found to be larger in models using the Epanechnikov kernel.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
