High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion
Abstract
We consider the problem of high-dimensional Gaussian graphical model selection. We identify a set of graphs for which an efficient estimation algorithm exists, and this algorithm is based on thresholding of empirical conditional covariances. Under a set of transparent conditions, we establish structural consistency (or sparsistency) for the proposed algorithm, when the number of samples n=omega(J_{min}^{-2} log p), where p is the number of variables and J_{min} is the minimum (absolute) edge potential of the graphical model. The sufficient conditions for sparsistency are based on the notion of walk-summability of the model and the presence of sparse local vertex separators in the underlying graph. We also derive novel non-asymptotic necessary conditions on the number of samples required for sparsistency.
Cite
@article{arxiv.1107.1270,
title = {High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion},
author = {Animashree Anandkumar and Vincent Y. F. Tan and Alan. S. Willsky},
journal= {arXiv preprint arXiv:1107.1270},
year = {2012}
}