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English(EN) Where Does the Union Bound Go? Best-Arm Identification and Strong FWER Control

统计学论文阐述最优臂识别与FWER控制

作者 PulseAugur 编辑部 · [1 个来源] ·

本文探讨了统计学中最优臂识别的理论基础，特别是研究了联合界（union bounds）的使用及其与家族错误率（FWER）控制的关系。文章阐明了在最优臂唯一的情况下，多重检验校正（如Bonferroni校正）是如何产生的。作者证明了两种常见假设方向的等价性，展示了多重检验问题在两者中如何以不同方式表现但依然存在。 AI

影响 该研究阐明了与机器学习算法相关的统计方法，这些算法涉及从多个选项中选择最佳选项。

排序理由 该条目是一篇在arXiv上发表的关于统计学方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

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统计学论文阐述最优臂识别与FWER控制

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该条目是一篇在arXiv上发表的关于统计学方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Rianne de Heide ·

    联合边界何去何从？最优手臂识别与强FWER控制

    arXiv:2608.19903v1 Announce Type: cross Abstract: In fixed-confidence best-arm identification, proofs often use a union bound across the competing arms. From a multiple-testing point of view this can look puzzling: if the best arm is unique, only one hypothesis of the form ``arm …

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