On Comparing and Classifying Several Independent Linear and Non-Linear Regression Models with Symmetric Errors

被引：41

作者：

Pan, Ji-Jun ^{[1
]}

Mahmoudi, Mohammad Reza ^{[2
]}

Baleanu, Dumitru ^{[3
]}

Maleki, Mohsen ^{[4
]}

机构：

[1] Normal Univ, Coll Math, Dianxi Sci & Technol, Lincang 677000, Peoples R China

[2] Fasa Univ, Fac Sci, Dept Stat, Fasa 7461686131, Iran

[3] Cankaya Univ Balgat, Fac Art & Sci, Dept Math, TR-06530 Ankara, Turkey

[4] Shiraz Univ, Fac Sci, Dept Stat, Shiraz 7194685115, Iran

来源：

SYMMETRY-BASEL | 2019年 / 11卷 / 06期

关键词：

comparison; Friedman test; linear regression; nonlinear regression; sign test; symmetric errors; Wilcoxon test; SIMULTANEOUS CONFIDENCE BANDS; EQUALITY; TESTS;

D O I：

10.3390/sym11060820

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

In many real world problems, science fields such as biology, computer science, data mining, electrical and mechanical engineering, and signal processing, researchers aim to compare and classify several regression models. In this paper, a computational approach, based on the non-parametric methods, is used to investigate the similarities, and to classify several linear and non-linear regression models with symmetric errors. The ability of each given approach is then evaluated using simulated and real world practical datasets.

引用

页数：10

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