Orthogonal one step greedy procedure for heteroscedastic linear models
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Publication:254223
DOI10.1016/j.jspi.2015.10.008zbMath1334.62109OpenAlexW1881980996MaRDI QIDQ254223
Publication date: 8 March 2016
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jspi.2015.10.008
Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)
Uses Software
Cites Work
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- The Adaptive Lasso and Its Oracle Properties
- The adaptive Lasso in high-dimensional sparse heteroscedastic models
- Grouping strategies and thresholding for high dimensional linear models
- Sparse Legendre expansions via \(\ell_1\)-minimization
- The road to deterministic matrices with the restricted isometry property
- Statistics for high-dimensional data. Methods, theory and applications.
- Concentration inequalities and model selection. Ecole d'Eté de Probabilités de Saint-Flour XXXIII -- 2003.
- Bridge estimators and the adaptive Lasso under heteroscedasticity
- CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
- Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit
- LOL selection in high dimension
- On stepwise pattern recovery of the fused Lasso
- The risk inflation criterion for multiple regression
- The adaptive and the thresholded Lasso for potentially misspecified models (and a lower bound for the Lasso)
- Preconditioning the Lasso for sign consistency
- The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
- Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain
- Decoding by Linear Programming
- Greed is Good: Algorithmic Results for Sparse Approximation
- Learning out of leaders
- Matrix Analysis
- Ideal spatial adaptation by wavelet shrinkage
- Uncertainty principles and ideal atomic decomposition
- Sure Independence Screening for Ultrahigh Dimensional Feature Space
- Matching pursuits with time-frequency dictionaries
- Minimax Rates of Estimation for High-Dimensional Linear Regression Over $\ell_q$-Balls
- Sparse Recovery With Orthogonal Matching Pursuit Under RIP
- Thresholding algorithms, maxisets and well-concentrated bases
- Adaptive greedy approximations
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