2011
DOI: 10.1103/physreve.83.051112
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Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique

Abstract: We present an approach, framed in information theory, to assess nonlinear causality between the subsystems of a whole stochastic or deterministic dynamical system. The approach follows a sequential procedure for nonuniform embedding of multivariate time series, whereby embedding vectors are built progressively on the basis of a minimization criterion applied to the entropy of the present state of the system conditioned to its past states. A corrected conditional entropy estimator compensating for the biasing e… Show more

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Cited by 227 publications
(256 citation statements)
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References 53 publications
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“…In the most general case, and when nonlinear effects are relevant, non-parametric approaches are recommended to yield model-free estimates of entropy and MI [29,[32][33][34]. However, the necessity to estimate entropies of variables of very high dimension may impair the reliability of model-free estimators, especially when short realizations of the processes are available [35].…”
Section: Computation Of Information Dynamicsmentioning
confidence: 99%
“…In the most general case, and when nonlinear effects are relevant, non-parametric approaches are recommended to yield model-free estimates of entropy and MI [29,[32][33][34]. However, the necessity to estimate entropies of variables of very high dimension may impair the reliability of model-free estimators, especially when short realizations of the processes are available [35].…”
Section: Computation Of Information Dynamicsmentioning
confidence: 99%
“…STE is a robust and computationally fast method to quantify the dominating direction of information flow between time series from structurally identical and non-identical coupled systems. Recently, Faes et al [52,53] introduced an enhanced version based on the corrected CE and a sequential procedure for non-uniform embedding to assess nonlinear GC in multivariate time series. It is actually a modified estimation of TE.…”
Section: (C) Entropymentioning
confidence: 99%
“…To investigate the functional relevance of coupling between the brain and the heart, CCE based on embedding the multivariate time series was used to detect nonlinear causal interactions between the brain and the heart. This method has been successfully used for assessment of cardiovascular regulatory mechanisms, such as analysis of the variability in heart period, arterial pressure, and respiratory rate; and investigation of the information flow across brain areas [28]. ECG data cannot be used in isolation to investigate the information flow between the brain and the heart, because the autonomic nervous system regulates cardiac performance by changing the RR interval.…”
Section: Discussionmentioning
confidence: 99%