Lasso, iterative feature selection and the correlation selector: oracle inequalities and numerical performances

DOI10.1214/08-EJS288zbMath1320.62084arXiv0710.4466OpenAlexW2950191036MaRDI QIDQ1951793

Pierre Alquier

Publication date: 24 May 2013

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

Full work available at URL: https://arxiv.org/abs/0710.4466

zbMATH Keywords

confidence regionsregression estimationLassostatistical learningshrinkage and thresholding methods

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric tolerance and confidence regions (62G15) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

Transductive versions of the Lasso and the Dantzig selector ⋮ Generalization of constraints for high dimensional regression problems

Uses Software

• R

Cites Work

• Unnamed Item
• Unnamed Item
• Iterative feature selection in least square regression estimation
• Some theoretical results on the grouped variables Lasso
• Symmetrization approach to concentration inequalities for empirical processes.
• Least angle regression. (With discussion)
• Honest variable selection in linear and logistic regression models via \(\ell _{1}\) and \(\ell _{1}+\ell _{2}\) penalization
• Simultaneous analysis of Lasso and Dantzig selector
• Sparsity oracle inequalities for the Lasso
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Approximation and learning by greedy algorithms
• A Statistical View of Some Chemometrics Regression Tools
• Density estimation with quadratic loss: a confidence intervals method
• Sparse Density Estimation with ℓ1 Penalties
• Model Selection and Estimation in Regression with Grouped Variables

This page was built for publication: Lasso, iterative feature selection and the correlation selector: oracle inequalities and numerical performances