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Activity Number: 355 - Modern Model Selection
Type: Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #313105
Title: The Complete Lasso Trade-Off Diagram: Above the Donoho–Tanner Phase Transition
Author(s): Yachong Yang* and Hua Wang and Weijie Su
Companies: Univ of Pennsylvania, Wharton School of Business and University of Pennsylvania, Statistics Department of Wharton and University of Pennsylvania
Keywords: Lasso path; false discovery rate; power; approximate message passing (AMP); Donoho–Tanner phase transition; variable selection

A fundamental problem in high-dimensional regression is to understand the trade-off between type I and type II errors or, equivalently, false discovery rate (FDR) and power in variable selection. To address this important problem, we offer the first complete diagram that distinguishes all pairs of FDR and power that can be asymptotically realized by the Lasso from the remaining pairs, in a regime of linear sparsity under random designs. The trade-off between the FDR and power characterized by our diagram holds no matter how strong the signals are. In particular, our results complete the earlier Lasso trade-off diagram of Su et al. (2017) by recognizing two simple constraints on the pairs of FDR and power. The improvement is more substantial when the regression problem is above the Donoho–Tanner phase transition. Finally, we present extensive simulation studies to confirm the sharpness of the complete Lasso trade-off diagram.

Authors who are presenting talks have a * after their name.

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