Low-dimensional approximation searching strategy for transfer entropy from non-uniform embedding

• Published in: PloS one Vol. 13; no. 3; p. e0194382
• Main Author: Zhang, Jian
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.03.2018; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology and Life Sciences; Causality; Computer and Information Sciences; Computer science; Computer simulation; Dynamical systems; Embedding; Entropy; Medicine and Health Sciences; Methods; Neurosciences; Physical Sciences; Physiology; Research and Analysis Methods; Researchers; Searching; Sensitivity analysis; Social Sciences; Time series; Variables
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0194382

Transfer entropy from non-uniform embedding is a popular tool for the inference of causal relationships among dynamical subsystems. In this study we present an approach that makes use of low-dimensional conditional mutual information quantities to decompose the original high-dimensional conditional mutual information in the searching procedure of non-uniform embedding for significant variables at different lags. We perform a series of simulation experiments to assess the sensitivity and specificity of our proposed method to demonstrate its advantage compared to previous algorithms. The results provide concrete evidence that low-dimensional approximations can help to improve the statistical accuracy of transfer entropy in multivariate causality analysis and yield a better performance over other methods. The proposed method is especially efficient as the data length grows.