The statistical theory of linear systems
The statistical theory of linear systems
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
A Linear Non-Gaussian Acyclic Model for Causal Discovery
The Journal of Machine Learning Research
Proceedings of the 25th international conference on Machine learning
New Introduction to Multiple Time Series Analysis
New Introduction to Multiple Time Series Analysis
A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model
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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The analysis of a relationship among variables in data generating systems is one of the important problems in machine learning. In this paper, we propose an approach for estimating a graphical representation of variables in data generating processes, based on the non-Gaussianity of external influences and an autoregressive moving-average (ARMA) model. The presented model consists of two parts, i.e., a classical structural-equation model for instantaneous effects and an ARMA model for lagged effects in processes, and is estimated through the analysis using the non-Gaussianity on the residual processes. As well as the recently proposed non-Gaussianity based method named LiNGAM analysis, the estimation by the proposed method has identifiability and consistency. We also address the relation of the estimated structure by our method to the Granger causality. Finally, we demonstrate analyses on the data containing both of the instantaneous causality and the Granger (temporal) causality by using our proposed method where the datasets for the demonstration cover both artificial and real physical systems.