Abstract
Sorted regularization has been incorporated into many methods for solving high-dimensional statistical estimation problems, including the SLOPE estimator in linear regression. In this paper, we study how this relatively new regularization technique improves variable selection by characterizing the optimal SLOPE trade-off between the false discovery proportion (FDP) and true positive proportion (TPP) or, equivalently, between measures of type I error and power. Assuming a regime of linear sparsity and working under Gaussian random designs, we obtain an upper bound on the optimal trade-off for SLOPE, showing its capability of breaking the Donoho–Tanner power limit. To put it into perspective, this limit is the highest possible power that the Lasso, which is perhaps the most popular -based method, can achieve even with arbitrarily strong effect sizes. Next, we derive a tight lower bound that delineates the fundamental limit of sorted regularization in optimally trading the FDP off for the TPP. Finally, we show that on any problem instance, SLOPE with a certain regularization sequence outperforms the Lasso, in the sense of having a smaller FDP, larger TPP and smaller estimation risk simultaneously. Our proofs are based on a novel technique that reduces a calculus of variations problem to a class of infinite-dimensional convex optimization problems and a very recent result from approximate message passing theory.
Funding Statement
Weijie Su was supported in part by NSF through CAREER DMS-1847415 and CCF-1934876, an Alfred Sloan Research Fellowship, and the Wharton Dean’s Research Fund.
Cynthia Rush was supported by NSF through CCF-1849883 and this work was done in part while the author was visiting the Simons Institute for the Theory of Computing.
Jason M. Klusowski was supported in part by NSF through DMS-2054808 and HDR TRIPODS DATA-INSPIRE DCCF-1934924.
Acknowledgments
Author names are listed alphabetically.
Citation
Zhiqi Bu. Jason M. Klusowski. Cynthia Rush. Weijie J. Su. "Characterizing the SLOPE trade-off: A variational perspective and the Donoho–Tanner limit." Ann. Statist. 51 (1) 33 - 61, February 2023. https://doi.org/10.1214/22-AOS2194
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