Summary: Separable penalties for sparse vector recovery are plentiful throughout statistical methodology and theory. Here, we confine attention to the problem of estimating sparse high-dimensional normal means. Separable penalized likelihood estimators are known to have a Bayesian interpretation as posterior modes under independent product priors. Such estimators can achieve rate-minimax performance when the correct level of sparsity is known. A fully Bayes approach, on the other hand, mixes the product priors over a shared complexity parameter. These constructions can yield a self-adaptive posterior that achieves rate-minimax performance when the sparsity level is unknown. Such optimality has also been established for posterior mean functionals. However, less is known about posterior modes in these setups. Ultimately, the mixing priors render the coordinates dependent through a penalty that is no longer separable. By tying the coordinates together, the hope is to gain adaptivity and achieve automatic hyperparameter tuning. Here, we study two examples of fully Bayes penalties: the fully Bayes LASSO and the fully Bayes Spike-and-Slab LASSO of Ročková and George (The Spike-and-Slab LASSO, Submitted). We discuss discrepancies and highlight the benefits of the two-group prior variant. We develop an Appell function apparatus for coping with adaptive selection thresholds. We show that the fully Bayes treatment of a complexity parameter is tantamount to oracle hyperparameter choice for sparse normal mean estimation.
For the entire collection see [Zbl 1341.62038].