Estimation of covariance matrices based on hierarchical inverse-Wishart priors

2011/06/16 by Bouriga, Mathilde, Féron, Olivier
#FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.1106.3203

Abstract

This paper focuses on Bayesian shrinkage for covariance matrix estimation. We examine posterior properties and frequentist risks of Bayesian estimators based on new hierarchical inverse-Wishart priors. More precisely, we give the existence conditions of the posterior distributions. Advantages in terms of numerical simulations of posteriors are shown. A simulation study illustrates the performance of the estimation procedures under three loss functions for relevant sample sizes and various covariance structures.

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