Machine Learning

, Volume 80, Issue 2–3, pp 295–319 | Cite as

Time varying undirected graphs



Undirected graphs are often used to describe high dimensional distributions. Under sparsity conditions, the graph can be estimated using 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 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.


Graph selection 1 regularization High dimensional asymptotics Risk consistency 


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Copyright information

© The Author(s) 2010

Authors and Affiliations

  1. 1.Seminar für StatistikETH Zürich HG G 10.2ZürichSwitzerland
  2. 2.Computer Science DepartmentCarnegie Mellon UniversityPittsburghUSA
  3. 3.Department of StatisticsCarnegie Mellon UniversityPittsburghUSA

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