Debiasing convex regularized estimators and interval estimation in linear models

DOI10.1214/22-aos2243arXiv1912.11943OpenAlexW3203574187MaRDI QIDQ6117025

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Publication date: 19 July 2023

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1912.11943

zbMATH Keywords

confidence intervalscentral limit theorembias correctionvariance estimationLassohigh-dimensional linear modelsStein's formulaconvex regularizationGaussian Poincaré inequality

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Robustness and adaptive procedures (parametric inference) (62F35) Nonparametric tolerance and confidence regions (62G15)

Related Items (4)

Noisy linear inverse problems under convex constraints: exact risk asymptotics in high dimensions ⋮ Universality of regularized regression estimators in high dimensions ⋮ The Lasso with general Gaussian designs with applications to hypothesis testing ⋮ Asymptotic normality of robust \(M\)-estimators with convex penalty

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