Transductive versions of the Lasso and the Dantzig selector

DOI10.1016/j.jspi.2012.03.020zbMath1428.62312arXiv0906.0652OpenAlexW2964240626MaRDI QIDQ447611

Pierre Alquier, Mohamed Hebiri

Publication date: 4 September 2012

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0906.0652

zbMATH Keywords

high-dimensional datavariable selectionsparsityLassohigh-dimensional regression estimationtransduction

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (1)

On the prediction loss of the Lasso in the partially labeled setting

Uses Software

• hgam

Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• The Dantzig selector and sparsity oracle inequalities
• Generalization of constraints for high dimensional regression problems
• Some theoretical results on the grouped variables Lasso
• Lasso-type recovery of sparse representations for high-dimensional data
• Sparse recovery in convex hulls via entropy penalization
• High-dimensional additive modeling
• Estimating the dimension of a model
• Text classification from labeled and unlabeled documents using EM
• Least angle regression. (With discussion)
• Lasso, iterative feature selection and the correlation selector: oracle inequalities and numerical performances
• On the conditions used to prove oracle results for the Lasso
• Simultaneous analysis of Lasso and Dantzig selector
• High-dimensional generalized linear models and the lasso
• Smoothing \(\ell_1\)-penalized estimators for high-dimensional time-course data
• Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators
• Aggregation for Gaussian regression
• Pathwise coordinate optimization
• The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
• High-dimensional graphs and variable selection with the Lasso
• DASSO: Connections Between the Dantzig Selector and Lasso
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• On the Non-Negative Garrotte Estimator
• Aggregation by Exponential Weighting and Sharp Oracle Inequalities

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