Estimation of dynamic bivariate correlation using a weighted graph algorithm

Authors

Majnu John, Feinstein Institute of Medical Research
Yihren Wu, Hofstra University
Manjari Narayan, Stanford University
Aparna John, University of Texas at San Antonio
Toshikazu Ikuta, University of Mississippi
Janina Ferbinteanu, SUNY Downstate Health Sciences University

Document Type

Article

Publication Date

6-1-2020

Abstract

© 2020 by the authors. 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.

Relational Format

journal article

Recommended Citation

John, Majnu, et al. “Estimation of Dynamic Bivariate Correlation Using a Weighted Graph Algorithm.” Entropy, vol. 22, no. 6, June 2020, p. 617, doi:10.3390/e22060617.

DOI

10.3390/E22060617

Accessibility Status

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