Article ID Journal Published Year Pages File Type
1148769 Journal of Statistical Planning and Inference 2013 14 Pages PDF
Abstract

This paper focuses on Bayesian shrinkage methods 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 conditions for the existence 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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Physical Sciences and Engineering Mathematics Applied Mathematics
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