Correlation adjusted debiased Lasso: debiasing the Lasso with inaccurate covariate model

• 成果类型: Article
• 署名作者: Celentano, Michael; Montanari, Andrea
• 署名单位: University of California System; University of California Berkeley; Stanford University; Stanford University
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1093/jrsssb/qkae039
• 发表日期: 2024
• 页码: 1455-1482
• 关键词: confidence-intervals; phase-transitions; regression; geometry

摘要

We consider the problem of estimating a low-dimensional parameter in high-dimensional linear regression. Constructing an approximately unbiased estimate of the parameter of interest is a crucial step towards performing statistical inference. Several authors suggest to orthogonalize both the variable of interest and the outcome with respect to the nuisance variables, and then regress the residual outcome with respect to the residual variable. This is possible if the covariance structure of the regressors is perfectly known, or is sufficiently structured that it can be estimated accurately from data (e.g. the precision matrix is sufficiently sparse). Here we consider a regime in which the covariate model can only be estimated inaccurately, and hence existing debiasing approaches are not guaranteed to work. We propose the correlation adjusted debiased Lasso, which nearly eliminates this bias in some cases, including cases in which the estimation errors are neither negligible nor orthogonal.