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Granger causality is a widely-used criterion for analyzing interactions in large-scale networks.
Investigating causal relations by econometric models and cross-spectral methods
C. W. J. Granger · 1969
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Extensions of lipschitz maps into a hilbert space
William Johnson and Joram Lindenstrauss · 1984
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A general test for Granger causality: Bivariate model
E. Baek and W. Brock · 1992
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Estimating brain connectivity with varying-length time lags using a recurrent neural network
Y. Wang, K. Lin, Y. Qi, Q. Lian, S. Feng, Z. Wu, and G. Pan · 1992
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Testing for linear and nonlinear Granger causality in the stock price- volume relation
Craig Hiemstra and Jonathan D. Jones · 1994
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Long short-term memory
Sepp Hochreiter and Jargen Schmidhuber · 1997
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Herbert Jaeger · 2002
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A new statistic and practical guidelines for nonparametric Granger causality testing
Cees Diks and Valentyn Panchenko · 2006
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Recurrent neural networks are universal approximators
Anton Maximilian Schäfer and Hans Georg Zimmermann · 2006
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André Fujita, João R Sato, Miguel Garay, Rui Yamaguchi, Satoru Miyano, Mari Sogayar, and Carlos E Ferreira · 2007
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Kernel-Granger causality and the analysis of dynamical networks
D. Marinazzo, M. Pellicoro, and S. Stramaglia · 2008
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Generating realistic in silico gene networks for performance assessment of reverse engineering methods
Daniel Marbach, Thomas Schaffter, Claudio Mattiussi, and Dario Floreano · 2009
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Multivariate linear and nonlinear causality tests
Zhidong Bai, Wing-Keung Wong, and Bingzhi Zhang · 2010
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