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Time varying undirected graphs
Open accessAbstract
Undirected graphs are often used to describe high dimensional distributions. Under sparsity conditions, the graph can be estimated using ℓ1 penalization methods. However, current methods assume that the data are independent and identically distributed. If the distribution, and hence the graph, evolves over time then the data are not longer identically distributed. In this paper we develop a nonparametric method for estimating time varying graphical structure for multivariate Gaussian distributions using an ℓ1 regularization method, and show that, as long as the covariances change smoothly over time, we can estimate the covariance matrix well (in predictive risk) even when p is large.
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Cited by
mgm: Estimating Time-Varying Mixed Graphical Models in High-Dimensional Data
Haslbeck, J. M. B., & Waldorp, L. J. (2020) · Journal of Statistical Software · doi:10.18637/jss.v093.i08 · Cited by 746
High-dimensional covariance estimation by minimizing ℓ1-penalized log-determinant divergence
Ravikumar, P., Wainwright, M. J., Raskutti, G., & Yu, B. (2011) · Electronic Journal of Statistics · doi:10.1214/11-ejs631 · Cited by 744
Sparse Group Lasso: Consistency and Climate Applications
Chatterjee, S., Steinhaeuser, K., Banerjee, A., Chatterjee, S., & Ganguly, A. (2012) · Proceedings of the 2012 SIAM International Conference on Data Mining · doi:10.1137/1.9781611972825.5 · Cited by 72
Metadata
- Authors
- Zhou, S., Lafferty, J., & Wasserman, L.
- Venue
- Machine Learning
- Published
- 2010-04-27
- Volume
- 80
- Issue
- 2-3
- Pages
- 295-319
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1972127338
- Cited by
- 195
- References
- 0
- FWCI
- 9.70
- Type
- journal article
- Language
- en
- Open access
- bronze
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