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A novel piecewise-linear method for detecting associations between variables

Detecting the association between two variables is necessary and meaningful in the era of big data. There are many measures to detect the association between them, some detect linear association, e.g., simple and fast Pearson correlation coefficient, and others detect nonlinear association, e.g., co...

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Detalles Bibliográficos
Autores principales: Wang, Panru, Zhang, Junying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449123/
https://www.ncbi.nlm.nih.gov/pubmed/37616293
http://dx.doi.org/10.1371/journal.pone.0290280
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author Wang, Panru
Zhang, Junying
author_facet Wang, Panru
Zhang, Junying
author_sort Wang, Panru
collection PubMed
description Detecting the association between two variables is necessary and meaningful in the era of big data. There are many measures to detect the association between them, some detect linear association, e.g., simple and fast Pearson correlation coefficient, and others detect nonlinear association, e.g., computationally expensive and imprecise maximal information coefficient (MIC). In our study, we proposed a novel maximal association coefficient (MAC) based on the idea that any nonlinear association can be considered to be composed of some piecewise-linear ones, which detects linear or nonlinear association between two variables through Pearson coefficient. We conduct experiments on some simulation data, with the results show that the MAC has both generality and equitability. In addition, we also apply MAC method to two real datasets, the major-league baseball dataset from Baseball Prospectus and dataset of credit card clients’ default, to detect the association strength of pairs of variables in these two datasets respectively. The experimental results show that the MAC can be used to detect the association between two variables, and it is computationally inexpensive and precise than MIC, which may be potentially important for follow-up data analysis and the conclusion of data analysis in the future.
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spelling pubmed-104491232023-08-25 A novel piecewise-linear method for detecting associations between variables Wang, Panru Zhang, Junying PLoS One Research Article Detecting the association between two variables is necessary and meaningful in the era of big data. There are many measures to detect the association between them, some detect linear association, e.g., simple and fast Pearson correlation coefficient, and others detect nonlinear association, e.g., computationally expensive and imprecise maximal information coefficient (MIC). In our study, we proposed a novel maximal association coefficient (MAC) based on the idea that any nonlinear association can be considered to be composed of some piecewise-linear ones, which detects linear or nonlinear association between two variables through Pearson coefficient. We conduct experiments on some simulation data, with the results show that the MAC has both generality and equitability. In addition, we also apply MAC method to two real datasets, the major-league baseball dataset from Baseball Prospectus and dataset of credit card clients’ default, to detect the association strength of pairs of variables in these two datasets respectively. The experimental results show that the MAC can be used to detect the association between two variables, and it is computationally inexpensive and precise than MIC, which may be potentially important for follow-up data analysis and the conclusion of data analysis in the future. Public Library of Science 2023-08-24 /pmc/articles/PMC10449123/ /pubmed/37616293 http://dx.doi.org/10.1371/journal.pone.0290280 Text en © 2023 Wang, Zhang https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wang, Panru
Zhang, Junying
A novel piecewise-linear method for detecting associations between variables
title A novel piecewise-linear method for detecting associations between variables
title_full A novel piecewise-linear method for detecting associations between variables
title_fullStr A novel piecewise-linear method for detecting associations between variables
title_full_unstemmed A novel piecewise-linear method for detecting associations between variables
title_short A novel piecewise-linear method for detecting associations between variables
title_sort novel piecewise-linear method for detecting associations between variables
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10449123/
https://www.ncbi.nlm.nih.gov/pubmed/37616293
http://dx.doi.org/10.1371/journal.pone.0290280
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