Association Factor for Identifying Linear and Nonlinear Correlations in Noisy Conditions
Background: In data analysis and machine learning, we often need to identify and quantify the correlation between variables. Although Pearson’s correlation coefficient has been widely used, its value is reliable only for linear relationships and Distance correlation was introduced to address this sh...
Main Authors: | Nezamoddin N. Kachouie, Wejdan Deebani |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-04-01
|
Series: | Entropy |
Subjects: | |
Online Access: | https://www.mdpi.com/1099-4300/22/4/440 |
Similar Items
-
An Econophysics Study of the S&P Global Clean Energy Index
by: Paulo Ferreira, et al.
Published: (2020-01-01) -
Multiple scaling behaviour and nonlinear traits in music scores
by: Alfredo González-Espinoza, et al.
Published: (2017-01-01) -
Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies
by: Natália Costa, et al.
Published: (2019-09-01) -
Quantitative Analysis of Elbow Range of Motion Variability due to Muscular Fatigue
by: Mohammad Ali Sanjari, et al.
Published: (2014-01-01) -
Dynamic Connectivity in a Financial Network Using Time-Varying DCCA Correlation Coefficients
by: Paulo Ferreira, et al.
Published: (2021-06-01)