Unifying Sparse Estimation of Precision and Covariance Matrices

Abstract

Penalized maximum likelihood is a widely used approach to estimate a sparse precision or a sparse covariance matrix. A zero in the precision matrix encodes a conditional independence and a zero in the covariance matrix a marginal independence, and the two estimators have been developed separately, each with its own algorithm. We show that these two problems can be unified via a single reparametrization of the last column of either matrix. This reparametrization decouples the likelihood function for each problem to a one-dimensional minimization followed by a penalized multiple regression. Our approach applies to any penalty that is separable in the off-diagonal entries and linear on the diagonal. When the penalty is the ${\ell }_1$ norm, it unifies the graphical Lasso and the covariance graphical Lasso algorithms.