Machine Learning

, Volume 80, Issue 2, pp 295–319

Time varying undirected graphs

Authors

    • Seminar für StatistikETH Zürich HG G 10.2
  • John Lafferty
    • Computer Science DepartmentCarnegie Mellon University
  • Larry Wasserman
    • Department of StatisticsCarnegie Mellon University
Article

DOI: 10.1007/s10994-010-5180-0

Cite this article as:
Zhou, S., Lafferty, J. & Wasserman, L. Mach Learn (2010) 80: 295. doi:10.1007/s10994-010-5180-0

Abstract

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.

Keywords

Graph selection1 regularizationHigh dimensional asymptoticsRisk consistency
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Copyright information

© The Author(s) 2010