Flexible covariance estimation in graphical Gaussian models

TL;DR

This paper introduces flexible Bayesian covariance estimators for graphical Gaussian models that avoid complex computations and adaptively shrink different parts of the covariance matrix, improving high-dimensional estimation.

Contribution

It develops a new class of Bayes estimators based on the $W_{P_G}$ family, providing closed-form solutions and enhanced flexibility over traditional methods.

Findings

Estimators perform well in high-dimensional settings.

Flexible shape parameters improve covariance estimation.

Numerical examples demonstrate superior risk properties.

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.