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English(EN) When does conformal calibration need censoring weights? Cause-of-failure prediction sets under competing risks

新研究探讨了具有删失的竞争风险的共形预测

作者 PulseAugur 编辑部 · [1 个来源] ·

本文探讨了竞争风险的共形预测方法，重点关注删失如何影响覆盖率保证。作者研究了仅考虑完全观察到的数据的完全案例校准的影响，并证明它可能导致覆盖率不足，尤其是在删失普遍存在的情况下。他们提出使用源自正确指定的删失模型的权重来维持标称覆盖率，扩展了先前关于失效标签的工作，并建立了考虑删失模型错误的有限样本覆盖率下界。 AI

排序理由 关于统计学方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新研究探讨了具有删失的竞争风险的共形预测

本文如何被排名

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3 / 100
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关于统计学方法的学术论文。[lever_c_demoted from research: ic=1 ai=0.4]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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paper, other
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Sunny Yang, Weiyan Zhao ·

    何时需要对共形校准进行审查权重？竞争风险下的失效原因预测集

    arXiv:2610.08602v1 Announce Type: cross Abstract: Split conformal prediction sets for competing-risks labels at a fixed horizon require calibration labels that right censoring can leave unobserved. Complete-case calibration guarantees coverage for the label-complete subpopulation…

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