A Partial Correlation Screening Approach for Controlling the False Positive Rate in Sparse Gaussian Graphical Models.

ABSTRACT: Gaussian Graphical Models (GGMs) are extensively used in many research areas, such as genomics, proteomics, neuroimaging, and psychology, to study the partial correlation structure of a set of variables. This structure is visualized by drawing an undirected network, in which the variables constitute the nodes and the partial correlations the edges. In many applications, it makes sense to impose sparsity (i.e., some of the partial correlations are forced to zero) as sparsity is theoretically meaningful and/or because it improves the predictive accuracy of the fitted model. However, as we will show by means of extensive simulations, state-of-the-art estimation approaches for imposing sparsity on GGMs, such as the Graphical lasso, ℓ₁ regularized nodewise regression, and joint spars

SUBMITTER: Lafit G 

PROVIDER: S-EPMC6882820 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature