Pairwise measures of causal direction in linear non-gaussian acyclic models

Journal of Machine Learning Research

Volumn 13, Issue , 2010, Pages 1-16

  Hyvärinen, Aapo ^a  

^a UNIVERSITY OF HELSINKI   (Finland)

Author keywords

Bayesian networks; Cumulants; Independent component analysis; Non gaussianity; Structural equation models

Indexed keywords

CUMULANT-BASED MEASURES; CUMULANTS; FIRST-ORDER APPROXIMATIONS; LIKELIHOOD RATIOS; NON-GAUSSIAN RANDOMS; NONGAUSSIANITY; PRESENCE OF MEASUREMENT NOISE; STRUCTURAL EQUATION MODELS;

BAYESIAN NETWORKS; INDEPENDENT COMPONENT ANALYSIS; LEARNING SYSTEMS;

GAUSSIAN NOISE (ELECTRONIC);

EID: 84867674525     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (11)

1. Y. Dodge and V. Rousson. On asymmetric properties of the correlation coefficient in the regression setting. The American Statistician, 55:51-54, 2001.
2. A. Hyvärinen, J. Karhunen, and E. Oja. Independent Component Analysis. Wiley Interscience, 2001.
3. A. Hyvärinen, K. Zhang, S. Shimizu, and P. O. Hoyer. Estimation of a structural vector autoregression model using non-gaussianity. J. of Machine Learning Research, 11:1709- 1731, 2010.
4. J. Karvanen and V. Koivunen. Blind separation methods based on pearson system and its extensions. Signal Processing, 82(4):663-573, 2002.
5. D.-T. Pham and P. Garrat. Blind separation of mixture of independent sources through a quasi-maximum likelihood approach. IEEE Trans. on Signal Processing, 45(7):1712- 1725, 1997.
6. S. Shimizu and Y. Kano. Use of non-normality in structural equation modeling: Application to direction of causation. Journal of Statistical Planning and Inference, 138:3483-3491, 2008.
7. S. Shimizu, P. O. Hoyer, A. Hyvärinen, and A. Kerminen. A linear non-Gaussian acyclic model for causal discovery. J. of Machine Learning Research, 7:2003-2030, 2006.
8. S. Shimizu, A. Hyvärinen, Y. Kawahara, and T. Washio. A direct method for estimating a causal ordering in a linear non-gaussian acyclic model. In Proc. 25th Conference on Uncertainty in Artificial Intelligence (UAI2009), page 506513, Montréal, Canada, 2009.
9. S. M. Smith, K. L. Miller, G. Salimi-Khorshidi, M. Webster, C. F. Beckmann, T. E. Nichols, J. D. Ramsey, and M. W. Woolrich. Network modelling methods for fMRI. 2010. Submitted manuscript.
10. Y. Sogawa, S. Shimizu, A. Hyvärinen, T. Washio, T. Shimamura, and S. Imoto. Discovery of exogenous variables in data with more variables than observations. In Proc. Int. Conf. on Artificial Neural Networks (ICANN2010), Thessaloniki, Greece, 2010a. In press.
11. Y. Sogawa, S. Shimizu, Y. Kawahara, and T. Washio. An experimental comparison of linear non-gaussian causal discovery methods and their variants. In Proc. Int. Joint Conf. on Neural Networks (IJCNN2010), Barcelona, Spain, 2010b.