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- W2895916002 abstract "In this paper, we study a new notion of scaled minimaxity for sparse estimation in high-dimensional linear regression model. We present more optimistic lower bounds than the one given by the classical minimax theory and hence improve on existing results. We recover sharp results for the global minimaxity as a consequence of our study. Fixing the scale of the signal-to-noise ratio, we prove that the estimation error can be much smaller than the global minimax error. We construct a new optimal estimator for the scaled minimax sparse estimation. An optimal adaptive procedure is also described." @default.
- W2895916002 created "2018-10-26" @default.
- W2895916002 creator A5091567428 @default.
- W2895916002 date "2018-10-12" @default.
- W2895916002 modified "2023-09-27" @default.
- W2895916002 title "Interplay of minimax estimation and minimax support recovery under sparsity" @default.
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