Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality
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Luca Faes | Daniele Marinazzo | Sebastiano Stramaglia | Roberto Prevete | Alessandro Montalto | Giovanni Tessitore
[1] L. Faes,et al. Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.
[2] Dong Yu,et al. Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..
[3] Daniele Marinazzo,et al. Radial basis function approach to nonlinear Granger causality of time series. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.
[4] Viola Priesemann,et al. Measuring Information-Transfer Delays , 2013, PloS one.
[5] Umberto Triacca,et al. Detecting human influence on climate using neural networks based Granger causality , 2011 .
[6] G. Kane. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .
[7] Daniele Marinazzo,et al. Synergy and redundancy in the Granger causal analysis of dynamical networks , 2014, New Journal of Physics.
[8] Martin A. Riedmiller,et al. A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.
[9] James L. McClelland,et al. Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .
[10] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[11] C. Granger. Investigating causal relations by econometric models and cross-spectral methods , 1969 .
[12] Daniele Marinazzo,et al. Kernel method for nonlinear granger causality. , 2007, Physical review letters.
[13] A. Ledberg,et al. Framework to study dynamic dependencies in networks of interacting processes. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.
[14] Joseph T. Lizier,et al. Directed Information Measures in Neuroscience , 2014 .
[15] Schreiber,et al. Measuring information transfer , 2000, Physical review letters.
[16] M. Paluš,et al. Directionality of coupling from bivariate time series: how to avoid false causalities and missed connections. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.
[17] Luca Faes,et al. MuTE: A MATLAB Toolbox to Compare Established and Novel Estimators of the Multivariate Transfer Entropy , 2014, PloS one.
[18] L. Baccalá,et al. Methods in Brain Connectivity Inference through Multivariate Time Series Analysis , 2014 .
[19] Daniele Marinazzo,et al. Nonlinear parametric model for Granger causality of time series , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.
[20] Dimitris Kugiumtzis,et al. Non-uniform state space reconstruction and coupling detection , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.
[21] Steven L. Bressler,et al. Wiener–Granger Causality: A well established methodology , 2011, NeuroImage.