The covariance selection in Gaussian graphical models consists in selecting, based on a sample of a multivariate normal vector, all those pairs of variables that are conditionally dependent given the remaining variables. This problem is equivalent to estimate the graph identifying the nonzero elements on the off-diagonal entries of the precision matrix. There are different proposals to carry out covariance selection in high-dimensional Gaussian graphical models, such as neighborhood selection and Glasso, among others. In this paper we introduce a methodology for evaluating the performance of graph estimators, defining the notion of non-informative estimator. Through a simulation study, the empirical behavior of Glasso in different structures of the precision matrix is investigated and its performance is analyzed according to different degrees of density of the graph. Our proposal can be used for other covariance selection methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.