Asymptotically optimal empirical Bayes inference in a piecewise constant sequence model

Inference on high-dimensional parameters in structured linear models is an important statistical problem. In this paper, for the piecewise constant Gaussian sequence model, we develop a new empirical Bayes solution that enjoys adaptive minimax posterior concentration rates and, thanks to the conjugate form of the empirical prior, relatively simple posterior computations.