Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm
| dc.contributor.author | John, Majnu | |
| dc.contributor.author | Wu, Yihren | |
| dc.contributor.author | Narayan, Manjari | |
| dc.contributor.author | John, Aparna | |
| dc.contributor.author | Ikuta, Toshikazu | |
| dc.contributor.author | Ferbinteanu, Janina | |
| dc.date.accessioned | 2021-04-19T15:21:14Z | |
| dc.date.available | 2021-04-19T15:21:14Z | |
| dc.date.issued | 2020-06-02 | |
| dc.date.updated | 2021-04-19T15:21:14Z | |
| dc.description.abstract | Dynamic correlation is the correlation between two time series across time. Two approaches that currently exist in neuroscience literature for dynamic correlation estimation are the sliding window method and dynamic conditional correlation. In this paper, we first show the limitations of these two methods especially in the presence of extreme values. We present an alternate approach for dynamic correlation estimation based on a weighted graph and show using simulations and real data analyses the advantages of the new approach over the existing ones. We also provide some theoretical justifications and present a framework for quantifying uncertainty and testing hypotheses. | |
| dc.description.department | Electrical and Computer Engineering | |
| dc.identifier | doi: 10.3390/e22060617 | |
| dc.identifier.citation | Entropy 22 (6): 617 (2020) | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12588/498 | |
| dc.rights | Attribution 4.0 United States | |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | dynamic bivariate correlation | |
| dc.subject | dynamic correlation | |
| dc.subject | fMRI | |
| dc.subject | local field potential | |
| dc.subject | sliding window | |
| dc.subject | dynamic conditional correlation | |
| dc.subject | functional connectivity | |
| dc.title | Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm | |
| dc.type | Article |