A DFA-based bivariate regression model for estimating the dependence of PM2.5 among neighbouring cities

Abstract On the basis of detrended fluctuation analysis (DFA), we propose a new bivariate linear regression model. This new model provides estimators of multi-scale regression coefficients to measure the dependence between variables and corresponding variables of interest with multi-scales. Numerica...

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Autores principales:	Fang Wang, Lin Wang, Yuming Chen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/80ead77dcd33470e80087f8299794ec0
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spelling	oai:doaj.org-article:80ead77dcd33470e80087f8299794ec02021-12-02T16:08:25ZA DFA-based bivariate regression model for estimating the dependence of PM2.5 among neighbouring cities10.1038/s41598-018-25822-w2045-2322https://doaj.org/article/80ead77dcd33470e80087f8299794ec02018-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-25822-whttps://doaj.org/toc/2045-2322Abstract On the basis of detrended fluctuation analysis (DFA), we propose a new bivariate linear regression model. This new model provides estimators of multi-scale regression coefficients to measure the dependence between variables and corresponding variables of interest with multi-scales. Numerical tests are performed to illustrate that the proposed DFA-bsaed regression estimators are capable of accurately depicting the dependence between the variables of interest and can be used to identify different dependence at different time scales. We apply this model to analyze the PM2.5 series of three adjacent cities (Beijing, Tianjin, and Baoding) in Northern China. The estimated regression coefficients confirmed the dependence of PM2.5 among the three cities and illustrated that each city has different influence on the others at different seasons and at different time scales. Two statistics based on the scale-dependent t-statistic and the partial detrended cross-correlation coefficient are used to demonstrate the significance of the dependence. Three new scale-dependent evaluation indices show that the new DFA-based bivariate regression model can provide rich information on studied variables.Fang WangLin WangYuming ChenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-13 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fang Wang Lin Wang Yuming Chen A DFA-based bivariate regression model for estimating the dependence of PM2.5 among neighbouring cities
description	Abstract On the basis of detrended fluctuation analysis (DFA), we propose a new bivariate linear regression model. This new model provides estimators of multi-scale regression coefficients to measure the dependence between variables and corresponding variables of interest with multi-scales. Numerical tests are performed to illustrate that the proposed DFA-bsaed regression estimators are capable of accurately depicting the dependence between the variables of interest and can be used to identify different dependence at different time scales. We apply this model to analyze the PM2.5 series of three adjacent cities (Beijing, Tianjin, and Baoding) in Northern China. The estimated regression coefficients confirmed the dependence of PM2.5 among the three cities and illustrated that each city has different influence on the others at different seasons and at different time scales. Two statistics based on the scale-dependent t-statistic and the partial detrended cross-correlation coefficient are used to demonstrate the significance of the dependence. Three new scale-dependent evaluation indices show that the new DFA-based bivariate regression model can provide rich information on studied variables.
format	article
author	Fang Wang Lin Wang Yuming Chen
author_facet	Fang Wang Lin Wang Yuming Chen
author_sort	Fang Wang
title	A DFA-based bivariate regression model for estimating the dependence of PM2.5 among neighbouring cities
title_short	A DFA-based bivariate regression model for estimating the dependence of PM2.5 among neighbouring cities
title_full	A DFA-based bivariate regression model for estimating the dependence of PM2.5 among neighbouring cities
title_fullStr	A DFA-based bivariate regression model for estimating the dependence of PM2.5 among neighbouring cities
title_full_unstemmed	A DFA-based bivariate regression model for estimating the dependence of PM2.5 among neighbouring cities
title_sort	dfa-based bivariate regression model for estimating the dependence of pm2.5 among neighbouring cities
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/80ead77dcd33470e80087f8299794ec0
work_keys_str_mv	AT fangwang adfabasedbivariateregressionmodelforestimatingthedependenceofpm25amongneighbouringcities AT linwang adfabasedbivariateregressionmodelforestimatingthedependenceofpm25amongneighbouringcities AT yumingchen adfabasedbivariateregressionmodelforestimatingthedependenceofpm25amongneighbouringcities AT fangwang dfabasedbivariateregressionmodelforestimatingthedependenceofpm25amongneighbouringcities AT linwang dfabasedbivariateregressionmodelforestimatingthedependenceofpm25amongneighbouringcities AT yumingchen dfabasedbivariateregressionmodelforestimatingthedependenceofpm25amongneighbouringcities
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A DFA-based bivariate regression model for estimating the dependence of PM2.5 among neighbouring cities

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