Quantile Fuzzy Varying Coefficient Regression based on kernel function

被引：8

作者：

Khammar, Amir Hamzeh ^{[1
]}

Arefi, Mohsen ^{[1
]}

Akbari, Mohammad Ghasem ^{[1
]}

机构：

[1] Univ Birjand, Fac Math Sci & Stat, Dept Stat, Birjand, Iran

来源：

APPLIED SOFT COMPUTING | 2021年 / 107卷

关键词：

Fuzzy data; Fuzzy varying coefficient regression; Goodness of fit; Kernel function; Robust regression; Quantile loss function; LINEAR-REGRESSION; LEAST-SQUARES; MODEL;

D O I：

10.1016/j.asoc.2021.107313

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The fuzzy varying coefficient regression model is a generalized version of fuzzy linear regression model. This kind of model is flexible and adaptable than fuzzy linear regression model. In this paper, we introduce a fuzzy varying coefficient regression model based on the quantile loss function and under the kernel function. Based on the presented goodness of fit indices, we show that the proposed approach is robust under the outlier data. Some applications of this approach are studied on some data sets and a simulated data set. (C) 2021 Elsevier B.V. All rights reserved.

引用

页数：12

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