Robustness in sparse high-dimensional linear models: relative efficiency and robust approximate message passing

DOI10.1214/16-EJS1212zbMath1357.62215arXiv1507.08726OpenAlexW2563552746MaRDI QIDQ502845

Jelena Bradic

Publication date: 11 January 2017

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

Full work available at URL: https://arxiv.org/abs/1507.08726

zbMATH Keywords

robustnessefficiencysparsityLassoLADapproximate message passingAMP

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric robustness (62G35) Central limit and other weak theorems (60F05)

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Automatic bias correction for testing in high‐dimensional linear models ⋮ Detangling robustness in high dimensions: composite versus model-averaged estimation ⋮ A tradeoff between false discovery and true positive proportions for sparse high-dimensional logistic regression ⋮ Asymptotic risk and phase transition of \(l_1\)-penalized robust estimator ⋮ Overcoming the limitations of phase transition by higher order analysis of regularization techniques ⋮ Scale calibration for high-dimensional robust regression

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