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Open AccessArticle

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

1
College of Mathematics, Dianxi Science and Technology, Normal University, Lincang 677000, China
2
Department of Statistics, Faculty of Science, Fasa University, Fasa 74616 86131, Iran
3
Department of Mathematics, Faculty of Art and Sciences, Cankaya University Balgat, Ankara 06530, Turkey
4
Department of Statistics, Faculty of Science, Shiraz University, Shiraz 71946 85115, Iran
*
Author to whom correspondence should be addressed.
Symmetry 2019, 11(6), 820; https://doi.org/10.3390/sym11060820
Received: 30 May 2019 / Revised: 15 June 2019 / Accepted: 19 June 2019 / Published: 20 June 2019
(This article belongs to the Special Issue Symmetry in Applied Continuous Mechanics)
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Abstract

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. View Full-Text
Keywords: comparison; Friedman test; linear regression; nonlinear regression; sign test; symmetric errors; Wilcoxon test comparison; Friedman test; linear regression; nonlinear regression; sign test; symmetric errors; Wilcoxon test
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Pan, J.-J.; Mahmoudi, M.R.; Baleanu, D.; Maleki, M. On Comparing and Classifying Several Independent Linear and Non-Linear Regression Models with Symmetric Errors. Symmetry 2019, 11, 820.

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