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

Jian Zhang

PLOS ONE, 2018, vol. 13, issue 3, 1-24

Abstract: 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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0194382

DOI: 10.1371/journal.pone.0194382

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