Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1145503 | Journal of Multivariate Analysis | 2015 | 12 Pages |
Abstract
An open problem in graphical Gaussian models is to determine the smallest number of observations needed to guarantee the existence of the maximum likelihood estimator of the covariance matrix with probability one. In this paper we formulate a closely related problem in which the existence of the maximum likelihood estimator is guaranteed for all generic observations. We call the number determined by this problem the Gaussian rank of the graph representing the model. We prove that the Gaussian rank is strictly between the subgraph connectivity number and the graph degeneracy number. These bounds are sharper than the bounds known in the literature and furthermore computable in polynomial time.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Emanuel Ben-David,