LOL selection in high dimension

DOI10.1016/j.csda.2012.04.012zbMath1471.62144OpenAlexW2069730747MaRDI QIDQ1621355

Dominique Picard, Karine Tribouley, Mathilde Mougeot

Publication date: 8 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2012.04.012

zbMATH Keywords

linear modelvariable selectioninstrumental variablessparsityhigh dimension

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08)

Related Items

Orthogonal one step greedy procedure for heteroscedastic linear models

Uses Software

• CoSaMP

Cites Work

• Unnamed Item
• Unnamed Item
• Near-ideal model selection by \(\ell _{1}\) minimization
• High-dimensional variable selection
• CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
• Sparsity oracle inequalities for the Lasso
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• Approximation and learning by greedy algorithms
• Identification of Causal Effects Using Instrumental Variables
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
• Learning out of leaders
• Pushing the Limits of Contemporary Statistics: Contributions in Honor of Jayanta K. Ghosh
• Sure Independence Screening for Ultrahigh Dimensional Feature Space
• Nonparametric Instrumental Regression