Optimal False Discovery Control of Minimax Estimator

In the analysis of high dimensional regression models, there are two important objectives: statistical estimation and variable selection. In literature, most works focus on either optimal estimation, e.g., minimax $L_2$ error, or optimal selection behavior, e.g., minimax Hamming loss. However in this study, we investigate the subtle interplay between the estimation accuracy and selection behavior. Our result shows that an estimator's $L_2$ error rate critically depends on its performance of type I error control. Essentially, the minimax convergence rate of false discovery rate over all rate-minimax estimators is a polynomial of the true sparsity ratio. This result helps us to characterize the false positive control of rate-optimal estimators under different sparsity regimes. More specifically, under near-linear sparsity, the number of yielded false positives always explodes to infinity under worst scenario, but the false discovery rate still converges to 0; under linear sparsity, even the false discovery rate doesn't asymptotically converge to 0. On the other side, in order to asymptotically eliminate all false discoveries, the estimator must be sub-optimal in terms of its convergence rate. This work attempts to offer rigorous analysis on the incompatibility phenomenon between selection consistency and rate-minimaxity observed in the high dimensional regression literature.