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

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.

SUBMITTER: Zhang J

PROVIDER: S-EPMC5856354 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

Zhang Jian J

PloS one 20180316 3

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 specific ...[more]

PMID: 29547669

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