On Time Varying Undirected Graphs
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:407-415, 2011.
The time-varying multivariate Gaussian distribution and the undirected graph associated with it, as introduced in Zhou et al. (2008), provide a useful statistical framework for modeling complex dynamic networks. In many application domains, it is of high importance to estimate the graph structure of the model consistently for the purpose of scientific discovery. In this short note, we show that under suitable technical conditions the structure of the undirected graphical model can be consistently estimated in the high dimensional setting, when the dimensionality of the model is allowed to diverge with the sample size. The model selection consistency is shown for the procedure proposed in Zhou et al. (2008) and for the modified neighborhood selection procedure of Meinshausen and Bühlmann (2006).