Necessary and sufficient conditions for variable selection consistency of the Lasso in high dimensions

DOI10.1214/20-AOS1979zbMath1469.62307MaRDI QIDQ2039788

Soumendra Nath Lahiri

Publication date: 5 July 2021

Published in: The Annals of Statistics (Search for Journal in Brave)

zbMATH Keywords

regularizationasymptotic normalityoracle propertyirrepresentable condition

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05)

Related Items (4)

A Critical Review of LASSO and Its Derivatives for Variable Selection Under Dependence Among Covariates ⋮ The EAS approach to variable selection for multivariate response data in high-dimensional settings ⋮ Retire: robust expectile regression in high dimensions ⋮ Iteratively reweighted \(\ell_1\)-penalized robust regression

Uses Software

• glmnet

Cites Work

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