Adaptive Debiased Lasso in High-Dimensional Generalized Linear Models with Streaming Data

• 成果类型: Article; Early Access
• 署名作者: Han, Ruijian; Luo, Lan; Luo, Yuanhang; Lin, Yuanyuan; Huang, Jian
• 署名单位: Hong Kong Polytechnic University; Rutgers University System; Chinese University of Hong Kong; Hong Kong Polytechnic University; Hong Kong Polytechnic University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2641199
• 发表日期: 2026-05-27
• 关键词: Confidence interval; Lasso; One-pass algorithm; stochastic gradient descent; statistical-inference; variable selection; confidence-regions; parameters; estimators; tests

摘要

Online statistical inference facilitates real-time analysis of sequentially collected data, making it different from traditional methods that rely on static datasets. This article introduces a novel approach to online inference in high-dimensional generalized linear models, where we update regression coefficient estimates and their standard errors upon each new data arrival. In contrast to existing methods that either require full dataset access or large-dimensional summary statistics storage, our method operates in a single-pass mode, significantly reducing both time and space complexity. The core of our methodological innovation lies in an adaptive stochastic gradient descent algorithm tailored for dynamic objective functions, coupled with a novel online debiasing procedure. This allows us to maintain low-dimensional summary statistics while effectively controlling the optimization error introduced by the dynamically changing loss functions. We establish the asymptotic normality of our proposed Adaptive Debiased Lasso (ADL) estimator. We conduct extensive simulation experiments to show the statistical validity and computational efficiency of our ADL estimator across various settings. Its computational efficiency is further demonstrated via a real data application to the spam email classification. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.