发表机构
University of Illinois Urbana-Champaign; Northumbria University（伊利诺伊大学厄巴纳-香槟分校; 诺森比亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究竞争风险下共形预测集的校准问题，发现完全案例校准可能偏离名义覆盖率，而正确指定删失模型的权重可恢复覆盖率，并给出有限样本下界。
AI 中文摘要
在固定时间范围内，针对竞争风险标签的分裂共形预测集需要校准标签，而右删失可能导致这些标签未被观测到。完全案例校准保证了标签完整子群的覆盖率，但其总体覆盖率可能偏离任一方向，即使在真实类别概率下也是如此。我们研究了选择如何改变总体分位数附近的得分分布。在我们主要模拟系列中的真实得分处，当22%的受试者在时间范围内无事件时，独立抽取下完全案例校准在名义0.900下覆盖了0.8723。在使用逐次抽取归一化的48个进一步设计中，有4个设计通过将原因发生率估计为1减去原因特异性Nelson-Aalen累积风险的负指数，并将无事件概率作为裁剪和重新归一化的余数，使得完全案例校准的覆盖率至少低于名义值四个标准误差。在这些设计中，来自正确指定删失模型的权重使覆盖率保持在名义值附近。将Yi等人（2025）的论证扩展到原因标签，我们建立了有限样本覆盖率下界，并对删失模型误差给出了显式惩罚。错误指定删失模型也可能导致覆盖率不足。
英文摘要
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, but its population coverage can deviate in either direction, even at the true class probabilities. We study how selection changes the score distribution near the population quantile. At the true score in our main simulation family, with independent draws, complete-case calibration covers 0.8723 at a nominal 0.900 when 22% of subjects are event-free at the horizon. In 4 of 48 further designs using per-draw normalisation, estimating cause incidences as one minus the exponential of the negative of the cause-specific Nelson-Aalen cumulative hazards, with the event-free probability as the clipped and renormalised remainder, puts complete-case coverage at least four standard errors below nominal. In these designs, weights from a correctly specified censoring model keep coverage near nominal. Extending the argument of Yi et al. (2025) to a cause label, we establish a finite-sample coverage lower bound with an explicit penalty for censoring-model error. Misspecifying the censoring model can also lead to under-coverage.
Comments38 pages, 5 figures
