Journal
MACHINE LEARNING
Volume 80, Issue 2-3, Pages 295-319Publisher
SPRINGER
DOI: 10.1007/s10994-010-5180-0
Keywords
Graph selection; l(1) regularization; High dimensional asymptotics; Risk consistency
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Undirected graphs are often used to describe high dimensional distributions. Under sparsity conditions, the graph can be estimated using a (1) 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 a (1) 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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