An \(\{\ell_{1},\ell_{2},\ell_{\infty}\}\)-regularization approach to high-dimensional errors-in-variables models

DOI10.1214/15-EJS1095zbMath1397.62246arXiv1412.7216MaRDI QIDQ309537

Mathieu Rosenbaum, Alexandre Belloni, Alexandre B. Tsybakov

Publication date: 7 September 2016

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

Full work available at URL: https://arxiv.org/abs/1412.7216

zbMATH Keywords

linear regression modelestimation method

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05)

Related Items (9)

Poisson Regression With Error Corrupted High Dimensional Features ⋮ Calibrated zero-norm regularized LS estimator for high-dimensional error-in-variables regression ⋮ On high-dimensional Poisson models with measurement error: hypothesis testing for nonlinear nonconvex optimization ⋮ Sparse estimation in high-dimensional linear errors-in-variables regression via a covariate relaxation method ⋮ High dimensional semiparametric moment restriction models ⋮ Low-rank matrix estimation via nonconvex optimization methods in multi-response errors-in-variables regression ⋮ On Parameter Estimation for High Dimensional Errors-in-Variables Models ⋮ Rate optimal estimation and confidence intervals for high-dimensional regression with missing covariates ⋮ Tikhonov-Phillips regularizations in linear models with blurred design

Cites Work

• Measurement error in Lasso: impact and likelihood bias correction
• Sparse recovery under matrix uncertainty
• High-dimensional regression with noisy and missing data: provable guarantees with nonconvexity
• Pivotal Estimation in High-Dimensional Regression via Linear Programming
• Improved matrix uncertainty selector

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