Fuzzy semi-parametric partially linear model with fuzzy inputs and fuzzy outputs

•We use semi-parametric methods to improve fuzzy linear regression models.•We present a detailed comparison of proposed method via sumulation data.•Efficiency of proposed method is demonstrated via some real-world applications. A large number of accounting studies have focused on parametric or non-p...

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Bibliographic Details
Published in:Expert systems with applications 2017-04, Vol.71, p.230-239
Main Authors: Hesamian, G., Akbari, M.G., Asadollahi, M.
Format: Article
Language:English
Subjects:
Computer simulation
Curve fitting
Dependent variables
Fuzzy input
Fuzzy output
Fuzzy semi-parametric partially linear
Fuzzy sets
Fuzzy systems
Goodness-of-fit measure
Independent variables
Kernel method
Least absolute deviation
Linear equations
Mathematical models
Non-fuzzy coefficient
Optimal bandwidth
Regression analysis
Studies
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Summary:•We use semi-parametric methods to improve fuzzy linear regression models.•We present a detailed comparison of proposed method via sumulation data.•Efficiency of proposed method is demonstrated via some real-world applications. A large number of accounting studies have focused on parametric or non-parametric forms of fuzzy regression relationships between dependent and independent variables. Notably, semi-parametric partially linear model as a powerful tool to incorporate statistical parametric and non-parametric regression analyses has gained attentions in many real-life applications recently. However, fuzzy data find application in many real studies. This study is an investigation of semi-parametric partially linear model for such cases to improve the conventional fuzzy linear regression models with fuzzy inputs, fuzzy outputs, fuzzy smooth function and non-fuzzy coefficients. For this purpose, a hybrid procedure is suggested based on curve fitting methods and least absolutes deviations to estimate the fuzzy smooth function and fuzzy coefficients. The proposed method is also examined to be compared with a common fuzzy linear regression model via a simulation data set and some real fuzzy data sets. It is shown that the proposed fuzzy regression model performs more convenient and efficient results in regard to six goodness-of-fit criteria which concludes that the proposed model could be a rational substituted model of some common fuzzy regression models in many practical studies of fuzzy regression model in expert and intelligent systems.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2016.11.032