COMPARATIVE ANALYSIS FOR FUZZY NONPARAMETRIC REGRESSION MODELS

Münevvere YILDIZ; Memmedağa MEMMEDLİ

doi:https://doi.org/10.18038/estubtda.1033350

Makale detayları

COMPARATIVE ANALYSIS FOR FUZZY NONPARAMETRIC REGRESSION MODELS

Yazarlar:

Münevvere YILDIZ | Memmedağa MEMMEDLİ

Yayıncı:

Eskisehir Technical University

Dergi:

Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 2021 Vol.22 Issue 4, pp.353-365

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000416832

pISSN:

2667-4211

DOI:

https://doi.org/10.18038/estubtda.1033350

Özet:

 

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.

 


Statistical modeling is essential to revealing the relationships between variables. These statistical models can be classified as parametric and nonparametric methods in studies using crisp values. However, most of the collected data are inherently fuzzy. In this context, the fuzzy expression of methods using precise data is a matter of curiosity for researchers. The methods with fuzzy input and output variables have been developed for a long time. The study aims to describe nonparametric local polynomial regression models in fuzzy structure to examine the results for cases where the 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.

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Makale detayları

COMPARATIVE ANALYSIS FOR FUZZY NONPARAMETRIC REGRESSION MODELS

Yazarlar: Münevvere YILDIZ | Memmedağa MEMMEDLİ

Yayıncı: Eskisehir Technical University

Dergi: Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 2021 Vol.22 Issue 4, pp.353-365

Belge Tipi: Makale

Yayın Tarihi: 12/2021

Belge Dili: İngilizce

Document ID (Org No.): 2000416832

pISSN: 2667-4211

DOI: https://doi.org/10.18038/estubtda.1033350

Sayı

Yazarlar:

Münevvere YILDIZ | Memmedağa MEMMEDLİ

Yayıncı:

Eskisehir Technical University

Dergi:

Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering 2021 Vol.22 Issue 4, pp.353-365

Belge Tipi:

Makale

Yayın Tarihi:

12/2021

Belge Dili:

İngilizce

Document ID (Org No.):

2000416832

pISSN:

2667-4211

DOI:

https://doi.org/10.18038/estubtda.1033350