Estimating a Fuzzy Semi-parametric Regression Model with Fuzzy Inputs and Fuzzy Outputs
DOI:
https://doi.org/10.31272/jae.i148.1424Keywords:
fuzzy semi-parametric regression model, kernel smoothing method, cubic method, Goodness .of .fitAbstract
The Fuzzy Semi-Parametric Partial Linear Model is an essential model for data analysis because it consists of two parts: a parametric part and a nonparametric part. The research dealt with the method of estimating the parametric part using the Fuzzy Ordinary least Square method and estimating the nonparametric part using the Kernel Smoothing method using functions (Triangular, Gaussian, Epanechnikov) and using the simulation method using the MATLAB program to obtain the results for four sample sizes (50, 75, 150, 200) with a variance of (0.1, 0.5, 0.9) and the experiment was repeated 1000 times. The results showed that the Speckman-Gaussian method is the best because it has the lowest Goodness. Fit.
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