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Nonlinear prediction of fuzzy regression model based on quantile loss function

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Abstract

In this paper, a new approach is presented to fit a fuzzy regression model with the fuzzy coefficients when the explanatory variables and the response variable are as fuzzy numbers. In this approach, a nonlinear function is introduced to predict the response variables based on the kernel function. To estimate the parameters of regression model, the objective function is calculated using the quantile loss function on fuzzy numbers. To evaluate the goodness of fit of the optimal quantile fuzzy regression models, two indices are introduced using the similarity measures. Based on the presented results, the proposed fuzzy regression models have the perfect performances on the original data and also in the presences of different types of outliers.

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Authors and Affiliations

  1. Department of Statistics, Faculty of Mathematical Sciences and Statistics, University of Birjand, Birjand, Iran

    Mohsen Arefi & Amir Hamzeh Khammar

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  1. Mohsen Arefi
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  2. Amir Hamzeh Khammar
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Correspondence to Mohsen Arefi.

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Arefi, M., Khammar, A.H. Nonlinear prediction of fuzzy regression model based on quantile loss function. Soft Comput 28, 4861–4871 (2024). https://doi.org/10.1007/s00500-023-09190-w

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