Testing for nonlinearity in time series: the method of surrogate data
Conference proceedings on Interpretation of time series from nonlinear mechanical systems
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A neural net for blind separation of nonstationary signals
Neural Networks
Independent component analysis by general nonlinear Hebbian-like learning rules
Signal Processing - Special issue on neural networks
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Blind separation methods based on Pearson system and its extensions
Signal Processing
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A Linear Non-Gaussian Acyclic Model for Causal Discovery
The Journal of Machine Learning Research
Proceedings of the 25th international conference on Machine learning
Consistency of the Group Lasso and Multiple Kernel Learning
The Journal of Machine Learning Research
Estimation of causal effects using linear non-Gaussian causal models with hidden variables
International Journal of Approximate Reasoning
ICA with Sparse Connections: Revisited
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Causality Discovery with Additive Disturbances: An Information-Theoretical Perspective
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
The Journal of Machine Learning Research
Computers in Biology and Medicine
Pairwise likelihood ratios for estimation of non-Gaussian structural equation models
The Journal of Machine Learning Research
Multi-dimensional causal discovery
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Analysis of causal effects between continuous-valued variables typically uses either autoregressive models or structural equation models with instantaneous effects. Estimation of Gaussian, linear structural equation models poses serious identifiability problems, which is why it was recently proposed to use non-Gaussian models. Here, we show how to combine the non-Gaussian instantaneous model with autoregressive models. This is effectively what is called a structural vector autoregression (SVAR) model, and thus our work contributes to the long-standing problem of how to estimate SVAR's. We show that such a non-Gaussian model is identifiable without prior knowledge of network structure. We propose computationally efficient methods for estimating the model, as well as methods to assess the significance of the causal influences. The model is successfully applied on financial and brain imaging data.