Global-Local Shrinkage Priors for Asymptotic Point and Interval Estimation of Normal Means under Sparsity

The paper addresses asymptotic estimation of normal means under sparsity. The primary focus is estimation of multivariate normal means where we obtain exact asymptotic minimax error under global-local shrinkage prior. This extends the corresponding univariate work of Ghosh and Chakrabarti (2017). In addition, we obtain similar results for the Dirichlet-Laplace prior as considered in Bhattacharya, Pati, Pillai, and Dunson (2015). Also, following van der Pas, Szabo, and van der Vaart (2017), we have been able to derive credible sets for multivariate normal means under global-local priors.