An \(\{\ell_{1},\ell_{2},\ell_{\infty}\}\)-regularization approach to high-dimensional errors-in-variables models
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Publication:309537
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
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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