Improved bounds for square-root Lasso and square-root slope

DOI10.1214/18-EJS1410zbMath1473.62132arXiv1703.02907MaRDI QIDQ1746538

Alexis Derumigny

Publication date: 25 April 2018

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1703.02907

zbMATH Keywords

adaptivityhigh-dimensional statisticssparse linear regressionminimax ratessquare-root estimators

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05) Nonparametric estimation (62G05) Minimax procedures in statistical decision theory (62C20)

Related Items (3)

Iterative algorithm for discrete structure recovery ⋮ Estimation of the \(\ell_2\)-norm and testing in sparse linear regression with unknown variance ⋮ Unnamed Item

Cites Work

• Unnamed Item
• SLOPE is adaptive to unknown sparsity and asymptotically minimax
• SLOPE-adaptive variable selection via convex optimization
• Bounds on the prediction error of penalized least squares estimators with convex penalty
• Regularization and the small-ball method. I: Sparse recovery
• Slope meets Lasso: improved oracle bounds and optimality
• Pivotal estimation via square-root lasso in nonparametric regression
• Simultaneous analysis of Lasso and Dantzig selector
• Square-root lasso: pivotal recovery of sparse signals via conic programming
• Scaled sparse linear regression
• Sharp Oracle Inequalities for Square Root Regularization

This page was built for publication: Improved bounds for square-root Lasso and square-root slope