Asymptotically minimax empirical Bayes estimation of a sparse normal mean vector
Asymptotically minimax empirical Bayes estimation of a sparse normal mean vector
For the important classical problem of inference on a sparse high-dimensional normal mean vector, we propose a novel empirical Bayes model that admits a posterior distribution with desirable properties under mild conditions. In particular, our empirical Bayes posterior distribution concentrates on balls, centered at the true mean vector, with squared …