Cross-validation with antithetic Gaussian randomization

• 成果类型: Article; Early Access
• 署名作者: Liu, Sifan; Panigrahi, Snigdha; Soloff, Jake A.
• 署名单位: Duke University; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412; 1467-9868
• DOI: 10.1093/jrsssb/qkag073
• 发表日期: 2026-05-14
• 关键词: antithetic sampling; Cross-validation; data fission; data splitting; model selection; variance reduction; stochastic-approximation; prediction error

摘要

We introduce a new cross-validation (CV) method based on an equicorrelated Gaussian randomization scheme. Our method is well-suited for problems where sample splitting is infeasible, either because the data violate the assumption of independent and identically distributed samples, or because there are insufficient samples to form representative train-test data pairs. In such problems, our method provides a simple, principled, and computationally efficient approach to estimating prediction error, often outperforming standard CV while requiring only a small number of repetitions. Drawing inspiration from recent splitting techniques like data fission and data thinning, our method constructs train-test data pairs using Gaussian randomization. Our main contribution is the introduction of an antithetic Gaussian randomization scheme, involving a carefully designed correlation structure among the randomization variables. We show theoretically that this antithetic construction can eliminate the bias of CV for a broad class of smooth prediction functions, without inflating variance. Through simulations across a range of data types and loss functions, we demonstrate that our estimator outperforms existing methods for prediction error estimation.