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

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, i...

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Bibliographic Details
Published in:Symmetry (Basel) 2019-06, Vol.11 (6), p.820
Main Authors: Ji-jun, Mahmoudi, Baleanu, Maleki
Format: Article
Language:English
Subjects:
Classification
comparison
Data mining
Datasets
Friedman test
Hypotheses
Investigations
linear regression
Mechanical engineering
nonlinear regression
Random variables
Regression analysis
Regression models
sign test
Signal processing
Simulation
Software
symmetric errors
Wilcoxon test
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Summary: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.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym11060820