Generalization of constraints for high dimensional regression problems

DOI10.1016/j.spl.2011.07.011zbMath1456.62137arXiv0811.0072OpenAlexW2084768096MaRDI QIDQ645414

Mohamed Hebiri, Pierre Alquier

Publication date: 15 November 2011

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0811.0072

zbMATH Keywords

high-dimensional datavariable selectionsparsityLassorestricted eigenvalue assumption

Mathematics Subject Classification ID

Parametric tolerance and confidence regions (62F25) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (4)

Estimation and variable selection in partial linear single index models with error-prone linear covariates ⋮ Transductive versions of the Lasso and the Dantzig selector ⋮ Gaussian approximations and multiplier bootstrap for maxima of sums of high-dimensional random vectors ⋮ Weaker regularity conditions and sparse recovery in high-dimensional regression

Uses Software

• hgam

Cites Work

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• Aggregation for Gaussian regression
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• High-dimensional graphs and variable selection with the Lasso
• Square-root lasso: pivotal recovery of sparse signals via conic programming
• Nonparametric statistical inverse problems
• Aggregation by Exponential Weighting and Sharp Oracle Inequalities

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