U-Statistic Reduction: Higher-Order Accurate Risk Control and Statistical-Computational Trade-Off

• 成果类型: Article; Early Access
• 署名作者: Shao, Meijia; Xia, Dong; Zhang, Yuan
• 署名单位: Hong Kong University of Science & Technology; University System of Ohio; Ohio State University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2448029
• 发表日期: 2025
• 关键词: edgeworth expansions; sign covariance; distance; INDEPENDENCE

摘要

U-statistics play central roles in many statistical learning tools but face the haunting issue of scalability. Despite extensive research on accelerating computation by U-statistic reduction, existing results almost exclusively focused on power analysis. Little work addresses risk control accuracy, which requires distinct and much more challenging techniques. In this article, we establish the first statistical inference procedure with provably higher-order accurate risk control for incomplete U-statistics. The sharpness of our new result enables us to reveal how risk control accuracy also trades off with speed, for the first time in literature, which complements the well-known variance-speed tradeoff. Our general framework converts the challenging and case-by-case analysis for many different designs into a surprisingly principled and routine computation. We conducted comprehensive numerical studies and observed results that validate our theory's sharpness. Our method also demonstrates effectiveness on real-world data applications. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.