Applications of the lasso and grouped lasso to the estimation of sparse graphical models

We propose several methods for estimating edge-sparse and nodesparse graphical models based on lasso and grouped lasso penalties. We develop ecien t algorithms for tting these models when the numbers of nodes and potential edges are large. We compare them to competing methods including the graphical lasso and SPACE (Peng, Wang, Zhou & Zhu 2008). Surprisingly, we nd that for edge selection, a simple method based on univariate screening of the elements of the empirical correlation matrix usually performs as well or better than all of the more complex methods proposed here and elsewhere. Running title: Applications of the lasso and grouped lasso