English(EN) On Large-Scale Multiple Testing Over Networks: A Non-Asymptotic Approach

新的聚合方法确保分布式网络测试中的有限样本FDR控制

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

一篇新研究论文介绍了交叉拟合贪婪聚合（CFGA），这是分布式网络多重检验的一项进展。该方法通过消除赢家诅咒偏差，解决了先前贪婪聚合算法的局限性，从而确保了有限样本的错误发现率（FDR）控制。该论文还提出了BONuS-GA和e-CFGA等变体，它们进一步优化了FDR控制和通信效率，并在各种样本量场景中显示出经验上的优势。 AI

排序理由该集群包含一篇关于新统计方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

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arXiv stat.ML TIER_1 English(EN) · Mehrdad Pournaderi · 2026-09-15 04:00

关于大规模网络多重检验：一种非渐近方法

arXiv:2609.14170v1 Announce Type: cross Abstract: Distributed multiple testing asks $N$ sites to control a global false discovery rate (FDR) under a tight communication budget. The greedy interval-aggregation algorithm of Pournaderi and Xiang (2024) solves this asymptotically but…

本文如何被排名

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关于大规模网络多重检验：一种非渐近方法

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