Optimal Variable Selection and Adaptive Noisy Compressed Sensing

DOI10.1109/TIT.2020.2965738zbMath1448.94081arXiv1809.03145MaRDI QIDQ5123873

Alexandre B. Tsybakov, Mohamed Ndaoud

Publication date: 29 September 2020

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1809.03145

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (8)

Iterative algorithm for discrete structure recovery ⋮ Partial recovery for top-\(k\) ranking: optimality of MLE and suboptimality of the spectral method ⋮ Improved RIP-based bounds for guaranteed performance of two compressed sensing algorithms ⋮ Variable selection, monotone likelihood ratio and group sparsity ⋮ Phase transitions for support recovery under local differential privacy ⋮ Which bridge estimator is the best for variable selection? ⋮ The all-or-nothing phenomenon in sparse linear regression ⋮ Optimal detection of the feature matching map in presence of noise and outliers

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