Joint estimation of linear non-Gaussian acyclic models

Neurocomputing

Volumn 81, Issue , 2012, Pages 104-107

  Shimizu, Shohei ^a  

^a OSAKA UNIVERSITY   (Japan)

Author keywords

Analysis of multiple groups; Causal discovery; Graphical models; Non Gaussianity; Structural equation models

Indexed keywords

CAUSAL DISCOVERY; GRAPHICAL MODEL; MULTIPLE-GROUP; NONGAUSSIANITY; STRUCTURAL EQUATION MODELS;

BEHAVIORAL RESEARCH; COMPUTER SIMULATION; ESTIMATION; GAUSSIAN NOISE (ELECTRONIC); TELECOMMUNICATION SERVICES;

MATHEMATICAL MODELS;

ARTICLE; CAUSAL MODELING; CONTROLLED STUDY; INTERMETHOD COMPARISON; LINEAR SYSTEM; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; PRIORITY JOURNAL; STRUCTURAL EQUATION MODELING;

EID: 84856360840     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.11.005     Document Type: Article

References (28)

1. Spirtes P., Glymour C., Scheines R. Causation, Prediction, and Search 1993, Springer Verlag, (2nd ed., MIT Press, 2000).
2. Bollen K. Structural Equations with Latent Variables 1989, John Wiley & Sons.
3. Hoyer P.O., Shimizu S., Kerminen A., Palviainen M. Estimation of causal effects using linear non-Gaussian causal models with hidden variables. Int. J. Approx. Reason. 2008, 49(2):362-378.
4. Shimizu S., Hoyer P.O., Hyvärinen A., Kerminen A. A linear non-Gaussian acyclic model for causal discovery. J. Mach. Learn. Res. 2006, 7:2003-2030.
5. Hyvärinen A., Zhang K., Shimizu S., Hoyer P.O. Estimation of a structural vector autoregressive model using non-Gaussianity. J. Mach. Learn. Res. 2010, 11:1709-1731.
6. P.O. Hoyer, D. Janzing, J. Mooij, J. Peters, B. Schölkopf, Nonlinear causal discovery with additive noise models, in: D. Koller, D. Schuurmans, Y. Bengio, L. Bottou (Eds.), Advances in Neural Information Processing Systems, vol. 21, 2009, pp. 689-696.
7. G. Lacerda, P. Spirtes, J. Ramsey, P.O. Hoyer, Discovering cyclic causal models by independent components analysis, in: Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI2008), 2008, pp. 366-374.
8. L. Faes, S. Erla, E. Tranquillini, D. Orrico, G. Nollo, An identifiable model to assess frequency-domain Granger causality in the presence of significant instantaneous interactions, in: Proceedings of the 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS2010), 2010, pp. 1699-1702.
9. Ferkingsta E., Lølanda A., Wilhelmsen M. Causal modeling and inference for electricity markets. Energy Econ. 2011, 33(3):404-412.
10. Sogawa Y., Shimizu S., Shimamura T., Hyvärinen A., Washio T., Imoto S. Estimating exogenous variables in data with more variables than observations. Neural Networks 2011, 24(8):875-880.
11. Shimizu S., Inazumi T., Sogawa Y., Hyvärinen A., Kawahara Y., Washio T., Hoyer P.O., Bollen K. DirectLiNGAM: a direct method for learning a linear non-Gaussian structural equation model. J. Mach. Learn. Res. 2011, 12:1225-1248.
12. Esposito F., Scarabino T., Hyvärinen A., Himberg J., Formisano E., Comani S., Tedeschi G., Goebel R., Seifritz E., Di Salle F. Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage 2005, 25(1):193-205.
13. Smith S.M., Miller K.L., Salimi-Khorshidi G., Webster M., Beckmann C.F., Nichols T.E., Ramsey J.D., Woolrich M.W. Network modelling methods for FMRI. Neuroimage 2011, 54(2):875-891.
14. Shimamura T., Imoto S., Yamaguchi R., Nagasaki M., Miyano S. Inferring dynamic gene networks under varying conditions for transcriptomic network comparison. Bioinformatics 2010, 26(8):1064-1072.
15. R.E. Tillman, Structure learning with independent non-identically distributed data, in: Proceedings of the 26th International Conference on Machine Learning (ICML2009), Montreal, Canada, ACM, 2009, pp. 1041-1048.
16. Lee S.Y., Tsui K.L. Covariance structure analysis in several populations. Psychometrika 1982, 47(3):297-308.
17. Ramsey J.D., Hanson S.J., Hanson C., Halchenko Y.O., Poldrack R.A., Glymour C. Six problems for causal inference from fMRI. Neuroimage 2010, 49(2):1545-1558.
18. I. Tsamardinos, A.P. Mariglis, Multi-source causal analysis: Learning Bayesian networks from multiple datasets, in: Proceedings of the 5th IFIP Conference on Artificial Intelligence Applications & Innovations (AIAI2009), Springer, 2009, pp. 479-490.
19. R.E. Tillman, P. Spirtes, Learning equivalence classes of directed acyclic latent variable models from multiple datasets with overlapping variables, in: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS2011), 2011.
20. S. Lee, J. Zhu, E. Xing, Adaptive multi-task Lasso: with application to eQTL detection, in: J. Lafferty, C.K.I. Williams, J. Shawe-Taylor, R. Zemel, A. Culotta (Eds.), Advances in Neural Information Processing Systems, vol. 23, 2010, pp. 1306-1314.
21. Ramsey J., Hanson S., Glymour C. Multi-subject search correctly identifies causal connections and most causal directions in the DCM models of the Smith et al. simulation study. Neuroimage 2011, 58(3):838-848.
22. Shimamura T., Imoto S., Shimada Y., Hosono Y., Niida A., Nagasaki M., Yamaguchi R., Takahashi T., Miyano S. A novel network profiling analysis reveals system changes in epithelial-mesenchymal transition. PLoS One 2011, 6(6):e20804.
23. Bach F.R., Jordan M.I. Kernel independent component analysis. J. Mach. Learn. Res. 2002, 3:1-48.
24. A. Hyvärinen, Pairwise measures of causal direction in linear non-Gaussian acyclic models, in: JMLR Workshop and Conference Proceedings (Proceedings of the 2nd Asian Conference on Machine Learning, ACML2010), vol. 13, 2010, pp. 1-16.
25. Y. Sogawa, S. Shimizu, Y. Kawahara, T. Washio, An experimental comparison of linear non-Gaussian causal discovery methods and their variants, in: Proceedings of 2010 International Joint Conference on Neural Networks (IJCNN2010), 2010, pp. 768-775.
26. Kalisch M., Bühlmann P. Estimating high-dimensional directed acyclic graphs with the PC-algorithm. J. Mach. Learn. Res. 2007, 8:613-636.
27. Gretton A., Györfi L. Consistent nonparametric tests of independence. J. Mach. Learn. Res. 2010, 11:1391-1423.
28. Y. Komatsu, S. Shimizu, H. Shimodaira, Assessing statistical reliability of LiNGAM via multiscale bootstrap, in: Proceedings of the International Conference on Artificial Neural Networks (ICANN2010), 2010, pp. 309-314.