Estimation Nonparametric Fuzzy Regression model using simulation
Abstract
In this research, three techniques of smoothing were used, local linear smoothing, kernel smoothing, k-Nearest Neighbor to estimate the model of the Fuzzy nonparametric regression. A dependent variable was represented by using Fuzzy Triangular Numbers . Based on the distance measure for the Fuzzy numbers which proposed by (Diamond) and we used the normal optimal smoothing method to select the optimal value of the smoothing parameter (h) where it was Fuzzified to fit the Fuzzy nonparametric model .
In order to demonstrate the best technique of smoothing, the simulation was used to compare these techniques using the (bias) criterion to determine the best estimate of the data generated by the simulation method and at the approved and proposed kernel functions. The comparison criterion showed that the lowest value of this criterion was when using the k- nearest neighbor technique at kernel functions (Gaussian).
Downloads
Downloads
Published
Issue
Section
License
The journal of Administration & Economics is an open- access journal that all contents are free of charge. Articles of this journal are licensed under the terms of the Creative Commons Attribution International Public License CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/legalcode) that licensees are unrestrictly allowedto search, download, share, distribute, print, or link to the full text of the articles, crawl them for indexing and reproduce any medium of the articles provided that they give the author(s) proper credits (citation). The journal allows the author(s) to retain the copyright of their published article.
Creative Commons-Attribution (BY) 
