Flexible covariance estimation in graphical Gaussian models

Abstract

In this paper, we propose a class of Bayes estimators for the covariance matrix of graphical Gaussian models Markov with respect to a decomposable graph $G$. Working with the $W_{P_G}$ family defined by Letac and Massam [Ann. Statist. 35 (2007) 1278--1323] we derive closed-form expressions for Bayes estimators under the entropy and squared-error losses. The $W_{P_G}$ family includes the classical inverse of the hyper inverse Wishart but has many more shape parameters, thus allowing for flexibility in differentially shrinking various parts of the covariance matrix. Moreover, using this family avoids recourse to MCMC, often infeasible in high-dimensional problems. We illustrate the performance of our estimators through a collection of numerical examples where we explore frequentist risk properties and the efficacy of graphs in the estimation of high-dimensional covariance structures.

Published in at http://dx.doi.org/10.1214/08-AOS619 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (3)

• High-dimensional graphs and variable selection with the Lasso
• Regularized estimation of large covariance matrices
• Wishart distributions for decomposable graphs

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