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Entropy 2017, 19(4), 150; doi:10.3390/e19040150

Minimum Sample Size for Reliable Causal Inference Using Transfer Entropy

National Institute for Space Research, São José dos Campos 12227-010, Brazil
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Academic Editors: António M. Lopes and J. A. Tenreiro Machado
Received: 20 February 2017 / Revised: 29 March 2017 / Accepted: 29 March 2017 / Published: 31 March 2017
(This article belongs to the Special Issue Complex Systems and Fractional Dynamics)
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Abstract

Transfer Entropy has been applied to experimental datasets to unveil causality between variables. In particular, its application to non-stationary systems has posed a great challenge due to restrictions on the sample size. Here, we have investigated the minimum sample size that produces a reliable causal inference. The methodology has been applied to two prototypical models: the linear model autoregressive-moving average and the non-linear logistic map. The relationship between the Transfer Entropy value and the sample size has been systematically examined. Additionally, we have shown the dependence of the reliable sample size and the strength of coupling between the variables. Our methodology offers a realistic lower bound for the sample size to produce a reliable outcome. View Full-Text
Keywords: transfer entropy; multiple comparison analysis; bias analysis; coupled logistic maps; coupled autoregressive-moving-average (ARMA) model transfer entropy; multiple comparison analysis; bias analysis; coupled logistic maps; coupled autoregressive-moving-average (ARMA) model
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Ramos, A.M.T.; Macau, E.E.N. Minimum Sample Size for Reliable Causal Inference Using Transfer Entropy. Entropy 2017, 19, 150.

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