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arXiv · 2601.18075

Maximum-Variance-Reduction Stratification for Improved Subsampling

Dingyi Wang·Haiying Wang·Qingpei Hu
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Abstract

Subsampling is a widely used and effective approach for addressing the computational challenges posed by massive datasets. Substantial progress has been made in developing non-uniform, probability-based subsampling schemes that prioritize more informative observations. We propose a novel stratification mechanism that can be combined with existing subsampling designs to further improve estimation efficiency. We establish the estimator's asymptotic normality and quantify the resulting efficiency gains, which enables a principled procedure for selecting stratification variables and interval boundaries that target reductions in asymptotic variance. The resulting algorithm, Maximum-Variance-Reduction Stratification (MVRS), achieves significant improvements in estimation efficiency while incurring only linear additional computational cost. MVRS is applicable to both non-uniform and uniform subsampling methods. Experiments on simulated and real datasets confirm that MVRS markedly reduces estimator variance and improves accuracy compared with existing subsampling methods.

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Dingyi Wang, Haiying Wang, Qingpei Hu. 2026-01-26. Maximum-Variance-Reduction Stratification for Improved Subsampling. https://arxiv.org/abs/2601.18075

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