Grouped graphical granger modeling methods for temporal causal modeling

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2009, Pages 577-585

  Lozano, Aurélie C ^a   Abe, Naoki ^a   Liu, Yan ^a   Rosset, Saharon ^b  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b TEL AVIV UNIVERSITY   (Israel)

Author keywords

Boosting; Granger causality; Graphical modeling; Temporal causal modeling; Variable group selection

Indexed keywords

BOOSTING; GRANGER CAUSALITY; GRAPHICAL MODELING; TEMPORAL CAUSAL MODELING; VARIABLE GROUP SELECTION;

TIME SERIES;

EID: 70350623328     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1557019.1557085     Document Type: Conference Paper

References (22)

1. Arnold, A., Liu, Y. and Abe, N. (2007), Temporal causal modeling with Graphical Granger methods. Proc. of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
2. Bach, F. (2008). Consistency of the group Lasso and multiple kernel learning, Technical report HAL-00164735, to appear in Journal of Machine Learning Research.
3. BÄuhlmann, P. and Yu, B. (2006). Sparse Boosing Journal of Machine Learning Research, 7, 1001-1024
4. BÄuhlmann, P. and Yu, B. (2003). Boosting with the L2 Loss: Regression and Classification. J. Amer. Statist. Assoc. 98, 324-340.
5. BÄuhlmann, P. (2007). Twin Boosting: improved feature selection and prediction, Preprint
6. BÄuhlmann, P. (2006). Boosting for high-dimensional linear models. Annals of Statistics 34, 559-583
7. Dahlhaus, R. and Eichler, M. (2003), Causality and graphical models in time series analysis. In P. Green, N. Hjort and S. Richardson (eds.): Highly structured stochastic systems, University Press, Oxford.
8. Eichler, M. (2007), Granger-causality and path diagrams for multivariate time series. Journal of Econometrics 137, 334-353.
9. Enders, W. (2003). Applied Econometric Time Series, 2nd Edition, John Wiley & Sons.
10. Hastie, T., Tibshirani R., Friedman JH (2001). The Elements of Statistical Learning. Springer.
11. Granger, C. (1980). Testing for causlity: A personal viewpoint. Journal of Economic Dynamics and Control 2, 329-352.
12. Hurvich, C., Simonoff, J. and Tsai, C.-L. (1998). Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J. R. Stat. Soc. Ser. B Stat. Methodol. 60 271-293. MR1616041
13. Lozano, A., Abe, N., Liu, Y, Rosset S. (2009). Grouped Graphical Granger Modeling for Gene Expression Regulatory Networks Discovery. In Proc. ISMB 2009.
14. Meinshausen, N. and BÄuhlmann, P. (2006). High-dimensional graphs and variable selection with the Lasso. Annals of Statistics 34, 1436-1462
15. Mukhopadhyay, N. D. and Chatterjee, S. (2007), Causality and pathway search in microarray time series experiment, Bioinformatics, 23(4).
16. Sambo F., Di Camillo B, Toffolo G. (2008) CNET: an algorithm for Reverse Engineering of Causal Gene Networks. NETTAB2008. Varenna, Italy.
17. Spirtes P., Glymour C., and Scheines, R. Causation, Prediction, and Search. MIT Press, New York, NY, second edition, 2000.
18. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J. Royal. Statist. Soc B., Vol. 58, No. 1, pages 267-288.
19. Yuan, M. and Lin, Y. (2006) Model selection and estimation in regression with grouped variables. J. R. Statist. Soc. B, 68, 49Ü67, pp. 49-67.
20. Zou, H. (2006) The Adaptive Lasso and its Oracle Properties. Journal of the American Statistical Society, Vol. 101, No. 476, pp. 1418-1429.
21. Zhao, P, Rocha, G. and Yu, B. (2006). Grouped and hierarchical model selection through composite absolute penalties. Annals of Statistics (to appear).
22. Zhou, N. and Zhu, J. (2007) Group variable selection via a hierarchical lasso and its oracle property. Unpublished manuscript.