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While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently nonlinear.
W. F. Sharpe, G. J. Alexander, and J. W. Bailey, Investments . Prentice Hall, 1968
1968
Earlier work this paper cites.
C. W. Granger, “Investigating causal relations by econometric models and cross-spectral methods,” Econometrica: Journal of the Econometric Society , pp. 424–438, 1969
1969
Earlier work this paper cites.
P. McCullagh and J. A. Nelder, Generalized Linear Models . CRC Press, 1989, vol. 37
1989
Earlier work this paper cites.
S. R. Chu, R. Shoureshi, and M. Tenorio, “Neural networks for system identification,” IEEE Control Systems Magazine , vol. 10, no. 3, pp. 31–35, 1990
1990
Earlier work this paper cites.
P. J. Werbos et al. , “Backpropagation through time: what it does and how to do it,” Proceedings of the IEEE , vol. 78, no. 10, pp. 1550–1560, 1990
1990
Earlier work this paper cites.
R. J. Williams and D. Zipser, “Gradient-based learning algorithms for recurrent,” Backpropagation: Theory, architectures, and applications , vol. 433, 1995
1995
Earlier work this paper cites.
R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society: Series B (Methodological) , vol. 58, no. 1, pp. 267–288, 1996
1996
Earlier work this paper cites.
S. Billings and S. Chen, “The determination of multivariable nonlinear models for dynamic systems using neural networks,” 1996
1996
Earlier work this paper cites.
V. Pavlovic, J. M. Rehg, and J. MacCormick, “Learning switching linear models of human motion,” in Advances in Neural Information Processing Systems , 2001, pp. 981–987
2001
Earlier work this paper cites.
G. P. Zhang, “Time series forecasting using a hybrid arima and neural network model,” Neurocomputing , vol. 50, pp. 159–175, 2003
2003
Earlier work this paper cites.
Ö. Kişi, “River flow modeling using artificial neural networks,” Journal of Hydrologic Engineering , vol. 9, no. 1, pp. 60–63, 2004
2004
Earlier work this paper cites.
H. Lütkepohl, New Introduction to Multiple Time Series Analysis . Springer Science & Business Media, 2005
2005
Earlier work this paper cites.
E. Hsu, K. Pulli, and J. Popović, “Style translation for human motion,” ACM Trans. Graph. , vol. 24, no. 3, pp. 1082–1089, Jul. 2005. [Online]. Available: http://doi.acm.org/10.1145/1073204.1073315
2005
Earlier work this paper cites.
M. Yuan and Y. Lin, “Model selection and estimation in regression with grouped variables,” Journal of the Royal Statistical Society: Series B (Statistical Methodology) , vol. 68, no. 1, pp. 49–67, 2006
2006
Earlier work this paper cites.
J. M. Wang, D. J. Fleet, and A. Hertzmann, “Gaussian process dynamical models for human motion,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 30, no. 2, pp. 283–298, 2007
2007
Earlier work this paper cites.
D. Marinazzo, M. Pellicoro, and S. Stramaglia, “Kernel-Granger causality and the analysis of dynamical networks,” Physical Review E , vol. 77, no. 5, p. 056215, 2008
2008
Earlier work this paper cites.
A. C. Lozano, N. Abe, Y. Liu, and S. Rosset, “Grouped graphical granger modeling methods for temporal causal modeling,” in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2009, pp. 577–586
2009
Earlier work this paper cites.
S. Lèbre, “Inferring dynamic genetic networks with low order independencies,” Statistical Applications in Genetics and Molecular Biology , vol. 8, no. 1, pp. 1–38, 2009
2009
Earlier work this paper cites.
CMU, “Carnegie mellon university motion capture database,” 2009, data retrieved from CMU, /http://mocap.cs.cmu.edu/
2009
Earlier work this paper cites.
A. C. Lozano, N. Abe, Y. Liu, and S. Rosset, “Grouped graphical granger modeling for gene expression regulatory networks discovery,” Bioinformatics , vol. 25, no. 12, pp. i110–i118, 2009
2009
Earlier work this paper cites.
O. Sporns, Networks of the Brain . MIT Press, 2010
2010
Earlier work this paper cites.
A. Fujita, P. Severino, J. R. Sato, and S. Miyano, “Granger causality in systems biology: modeling gene networks in time series microarray data using vector autoregressive models,” in Brazilian Symposium on Bioinformatics . Springer, 2010, pp. 13–24
2010
Earlier work this paper cites.
A. Shojaie and G. Michailidis, “Discovering graphical Granger causality using the truncating lasso penalty,” Bioinformatics , vol. 26, no. 18, pp. i517–i523, 2010
2010
Earlier work this paper cites.
T. Teräsvirta, D. Tjøstheim, C. W. J. Granger et al. , Modelling Nonlinear Economic Time Series . Oxford University Press Oxford, 2010
2010
Cited alongside, same era.
S. Kim and E. P. Xing, “Tree-guided group lasso for multi-task regression with structured sparsity.” in International Conference on Machine Learning , vol. 2. Citeseer, 2010, p. 1
2010
Cited alongside, same era.
A. Karimi and M. R. Paul, “Extensive chaos in the lorenz-96 model,” Chaos: An Interdisciplinary Journal of Nonlinear Science , vol. 20, no. 4, p. 043105, 2010
2010
Cited alongside, same era.
R. J. Prill, D. Marbach, J. Saez-Rodriguez, P. K. Sorger, L. G. Alexopoulos, X. Xue, N. D. Clarke, G. Altan-Bonnet, and G. Stolovitzky, “Towards a rigorous assessment of systems biology models: the dream3 challenges,” PloS One , vol. 5, no. 2, p. e9202, 2010
2010
Cited alongside, same era.
S. Basu, A. Shojaie, and G. Michailidis, “Network Granger causality with inherent grouping structure,” The Journal of Machine Learning Research , vol. 16, no. 1, pp. 417–453, 2015
2015
Later among the works it cites.
S. Basu, G. Michailidis et al. , “Regularized estimation in sparse high-dimensional time series models,” The Annals of Statistics , vol. 43, no. 4, pp. 1535–1567, 2015
2015
Later among the works it cites.
N. Lim, F. D’Alché-Buc, C. Auliac, and G. Michailidis, “Operator-valued kernel-based vector autoregressive models for network inference,” Machine Learning , vol. 99, no. 3, pp. 489–513, 2015
2015
Later among the works it cites.
K. Sameshima and L. A. Baccala, Methods in Brain Connectivity Inference Through Multivariate Time Series Analysis . CRC Press, 2016
2016
Later among the works it cites.
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R. Vicente, M. Wibral, M. Lindner, and G. Pipa, “Transfer entropy—a model-free measure of effective connectivity for the neurosciences,” Journal of Computational Neuroscience , vol. 30, no. 1, pp. 45–67, 2011
2011
Cited alongside, same era.
P. Bühlmann and S. Van De Geer, Statistics for High-Dimensional Data: Methods, Theory and Applications . Springer Science & Business Media, 2011
2011
Cited alongside, same era.
H. Tong, “Nonlinear time series analysis,” International Encyclopedia of Statistical Science , pp. 955–958, 2011
2011
Cited alongside, same era.
P.-O. Amblard and O. J. Michel, “On directed information theory and Granger causality graphs,” Journal of Computational Neuroscience , vol. 30, no. 1, pp. 7–16, 2011
2011
Cited alongside, same era.
J. Huang, T. Zhang, and D. Metaxas, “Learning with structured sparsity,” Journal of Machine Learning Research , vol. 12, no. Nov, pp. 3371–3412, 2011
2011
Cited alongside, same era.
R. Jenatton, J. Mairal, G. Obozinski, and F. Bach, “Proximal methods for hierarchical sparse coding,” Journal of Machine Learning Research , vol. 12, no. Jul, pp. 2297–2334, 2011
2011
Cited alongside, same era.
J. Runge, J. Heitzig, V. Petoukhov, and J. Kurths, “Escaping the curse of dimensionality in estimating multivariate transfer entropy,” Physical Review Letters , vol. 108, no. 25, p. 258701, 2012
2012
Cited alongside, same era.
A. Graves, “Supervised sequence labelling,” in Supervised Sequence Labelling with Recurrent Neural Networks . Springer, 2012, pp. 5–13
2012
Cited alongside, same era.
2016
Later among the works it cites.
J. M. Alvarez and M. Salzmann, “Learning the number of neurons in deep networks,” in Advances in Neural Information Processing Systems , 2016, pp. 2270–2278
2016
Later among the works it cites.
2016
Later among the works it cites.
2016
Later among the works it cites.
P. A. Stokes and P. L. Purdon, “A study of problems encountered in Granger causality analysis from a neuroscience perspective,” Proceedings of the National Academy of Sciences , vol. 114, no. 34, pp. E7063–E7072, 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
C. Louizos, K. Ullrich, and M. Welling, “Bayesian compression for deep learning,” in Advances in Neural Information Processing Systems , 2017, pp. 3288–3298
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
A. Sheikhattar, S. Miran, J. Liu, J. B. Fritz, S. A. Shamma, P. O. Kanold, and B. Babadi, “Extracting neuronal functional network dynamics via adaptive Granger causality analysis,” Proceedings of the National Academy of Sciences , vol. 115, no. 17, pp. E3869–E3878, 2018
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J. Lei, M. G’Sell, A. Rinaldo, R. J. Tibshirani, and L. Wasserman, “Distribution-free predictive inference for regression,” Journal of the American Statistical Association , vol. 113, no. 523, pp. 1094–1111, 2018
2018
Closest in time.
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