1 |
Arnold, A., Liu, Y., and Abe, N. (2008). Temporal causal modeling with graphical Granger methods, In Proceedings of the 13th ACM SIGKDD International Conference of Knowledge Discovery and Data Mining.
|
2 |
Cule, E., De Iorio, M. (2013). Ridge regression in prediction problems: automatic choice of the ridge parameter, Genetic Epidemiology, 37, 704-714.
DOI
|
3 |
Identification of synaptic connections in neural ensembles by graphical models, Journal of Neuroscience Methods, 77, 93-107.
DOI
|
4 |
Davis, R. A., Zang, P., and Zheng, T. (2015). Sparse vector autoregressive modeling, arXiv:1207.0520. Econometrica, 37, 424-438.
|
5 |
Hastie, T., Tibshirani, R., Wainwright, M. (2015). Statistical Learning with Sparsity: The Lasso and Generalizations, CRC press.
|
6 |
Huang, J., Ma, S., and Zhang, C.-H. (2008). Adaptive lasso for sparse high-dimensional regression models, Statistica Sincia, 18, 1608-1618.
|
7 |
Hsu, N.-J., Hung, H.-L., and Chang, Y.-M. (2008). Subset selection for vector autoregressive processes using lasso, Computational Statistics & Data Analysis, 52, 3645-3657.
DOI
|
8 |
Lozano, A. C., Abe, N., Liu, Y., and Rosset, S. (2009). Grouped graphical Granger modeling for gene expression regulatory networks discovery, Bioinformatics, 25, 110-118.
DOI
|
9 |
Lutkepohl, H. (2005). New Introduction to Multiple Time Series Analysis, Springer-Verlag, Berlin.
|
10 |
Song, S. and Bickel, P. J. (2011). Large vector auto regressions, arXiv:1106.3915.
|
11 |
Sims, C. A. (1980). Macroeconomics and reality, Econometrica: Journal of the Econometric Society, 1-48.
|
12 |
Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso, Journal of the Royal Statistical Society, Series B, 58, 267-288.
|
13 |
Zhang, J., Jeng, X. J., and Liu, H. (2008). Some Two-Step Procedures for Variable Selection in High-Dimensional Linear Regression, arXiv:0810.1644.
|
14 |
Zou, H. (2006). Adaptive lasso and its oracle properties, Journal of American Statistical Association, 101, 1418-1429.
DOI
|