Analyzing relationships among ARMA processes based on non-Gaussianity of external influences

Neurocomputing

Volumn 74, Issue 12-13, 2011, Pages 2212-2221

  Kawahara, Yoshinobu ^a   Shimizu, Shohei ^a   Washio, Takashi ^a  

^a OSAKA UNIVERSITY   (Japan)

Author keywords

Autoregressive moving average model; Graphical modeling; Non Gaussianity; Time series data

Indexed keywords

ARMA MODEL; ARMA PROCESS; AUTO-REGRESSIVE MOVING AVERAGE MODEL; AUTOREGRESSIVE MOVING AVERAGE; DATA SETS; EXTERNAL INFLUENCES; GENERATING SYSTEM; GRANGER CAUSALITY; GRAPHICAL MODELING; GRAPHICAL REPRESENTATIONS; IDENTIFIABILITY; IN-PROCESS; MACHINE-LEARNING; NON-GAUSSIANITY; REAL PHYSICAL SYSTEMS; TIME-SERIES DATA;

GAUSSIAN NOISE (ELECTRONIC); GRAPHIC METHODS;

TIME SERIES ANALYSIS;

ALGORITHM; ANALYTIC METHOD; ARTICLE; AUTOREGRESSIVE MOVING AVERAGE MODEL; DATA ANALYSIS; DATA BASE; INFORMATION PROCESSING; LINEAR NON GAUSSIAN ACYCLIC MODEL; LINEAR NON GAUSSIAN ACYCLIC MODEL ANALYSIS; PHYSICAL PARAMETERS; PRIORITY JOURNAL; STATISTICAL MODEL; STATISTICAL PARAMETERS; STRUCTURAL EQUATION MODELING; STRUCTURE ANALYSIS;

EID: 79955835112     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.02.008     Document Type: Article

References (23)

1. Opgen-Rhein R., Strimmer K. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Systems Biology 2007, 1:39.
2. Roebroeck A., Formisano E., Goebel R. Mapping directed influence over the brain using Granger causality and fMRI. NeuroImage 2005, 25:230-242.
3. Kim J., Zhu W., Chang L., Bentler P.M., Ernst T. Unified structural equation modeling approach for the analysis of multisubject multivariate functional MRI data. Human Brain Mapping 2007, 28:85-93.
4. T. Ide, A.C. Lozano, N. Abe, Y. Liu, Proximity-based anomaly detection using sparse structure learning, in: Proceedings of the SIAM 2009 International Conference on Data Mining (SDM'09), 2009, pp. 97-108.
5. Spirtes P., Glymour C., Scheines R. Causality, Prediction and Search 1993, Springer-Verlag.
6. Pearl J. Causality: Models, Reasoning and Inference 2000, Cambridge University Press.
7. Granger C.W.J. Investigating causal relations by econometric models and cross-spectral methods. Econometrica 1969, 37:424-438.
8. Shimizu S., Hoyer P.O., Hyvärinen A., Kerminen A. A linear non-Gaussian acyclic model for causal discovery. Journal of Machine Learning Research 2006, 7:2003-2030.
9. S. Shimizu, A. Hyvärinen, Y. Kawahara, T. Washio, A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model, in: Proceedings of the 25th International Conference on Uncertainty in Artificial Intelligence (UAI'09), 2009, pp. 506-513.
10. Boudjellaba H., Dufour J.M., Roy R. Testing causality between two vectors in multivariate autoregressive moving average models. Journal of the American Statistical Association 1992, 87:1082-1090.
11. A. Hyvärinen, S. Shimizu, P.O. Hoyer, Causal modeling combining instantaneous and lagged effects: an identifiable model based on non-Gaussianity, in: Proceedings of the 25th International Conference on Machine Learning (ICML'08), 2008, pp. 424-432.
12. Reinsel G.C. Elements of Multivariate Time Series Analysis 1997, Springer.
13. Box G.E.P., Jenkins G.M., Reinsel G.C. Time Series Analysis: Forecasting and Control 2008, John Wiley. fourth ed.
14. Sim C.A. Macroeconomics and reality. Econometrica 1980, 48(1):1-48.
15. Bollen K.A. Structural Equations with Latent Variables 1989, John Wiley.
16. Micceri T. The unicorn, the normal curve, and other improbable creatures. Psychological Bulletin 1989, 105(1):156-166.
17. Hyvärinen A., Karhunen J., Oja E. Independent Component Analysis 2001, Wiley Interscience.
18. Common P. Independent component analysis-a new concept?. Signal Processing 1994, 36:287-314.
19. Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 1999, 10(3):626-634.
20. Hannan E.J., Deistler M. The Statistical Theory of Linear Systems 1988, John Wiley.
21. Lütkepohl H. New Introduction to Multiple Time Series Analysis 2005, Springer-Verlag.
22. Shimizu S., Hoyer P.O., Hyvärinen A. Estimation of linear non-Gaussian acyclic models for latent factors. Neurocomputing 2009, 72:2024-2027.
23. Y. Kawahara, K. Bollen, S. Shimizu, T. Washio, Group LiNGAM: linear non-Gaussian acyclic models for sets of variables, ArXiv e-prints: 1006.5041, 2010.