Minimax rate of testing in sparse linear regression

DOI10.1134/S0005117919100047zbMath1456.62083arXiv1804.06494OpenAlexW2981302860WikidataQ127030149 ScholiaQ127030149MaRDI QIDQ2173042

Alexandre B. Tsybakov, Alexandra Carpentier, Olivier Collier, Yuhao Wang, Laetitia Comminges

Publication date: 22 April 2020

Published in: Automation and Remote Control (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1804.06494

zbMATH Keywords

signal detectionlinear regressionsparsity

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Linear regression; mixed models (62J05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items

High-dimensional asymptotics of likelihood ratio tests in the Gaussian sequence model under convex constraints, Adaptive robust estimation in sparse vector model, Estimation of the \(\ell_2\)-norm and testing in sparse linear regression with unknown variance, Optimal sparsity testing in linear regression model, Two-sample testing of high-dimensional linear regression coefficients via complementary sketching, Optimal detection of the feature matching map in presence of noise and outliers

Cites Work

• Unnamed Item
• Around the circular law
• Global testing under sparse alternatives: ANOVA, multiple comparisons and the higher criticism
• Nonparametric goodness-of-fit testing under Gaussian models
• Non-asymptotic minimax rates of testing in signal detection
• Higher criticism for detecting sparse heterogeneous mixtures.
• Minimax risks for sparse regressions: ultra-high dimensional phenomenons
• Detection boundary in sparse regression
• Random matrices: universality of ESDs and the circular law
• Minimax estimation of linear and quadratic functionals on sparsity classes