[论文] Non-Crossing Deep Quantile Regression for Distributional Survival Pred...

论文概要

研究领域: ML 作者: Shuai Huang, Zhe Qu, Zhaowei Hua et al. (6 authors) 发布时间: 2026-08-17 arXiv: 2608.16864

中文摘要

在生存分析中，协变量对事件风险的作用方式在早期和晚期失效时间往往不同，但基于风险和均值的摘要将这种变异压缩为单一数值。基于分位数的建模则在原始时间尺度上描述完整的条件分布，但现有删失数据方法要么不够灵活，要么产生逻辑上不一致的交叉分位数曲线。我们提出面向右删失数据的删失非交叉分位数（CNQ）框架，联合估计多个条件生存分位数并通过构造保证有效排序，由Kolmogorov-Arnold和Transformer骨干提供灵活性，并建立了在所有拟合分位数水平联合成立的有限样本超额风险界。在27个模拟设置和六个队列中，当条件分布不对称时，该框架比基于分位数、风险和树的竞争对手获得更低的pinball损失。在两个临床案例研究（METABRIC乳腺癌；FLCHAIN人群死亡率）中，它恢复了在生存分布上变化的协变量效应，这些效应会被单一风险比隐藏，并产生连贯的个体化分位数里程碑。

原文摘要

In survival analysis the way covariates act on the risk of an event often differs between early and late failure times, yet hazard- and mean-based summaries collapse this variation into a single number. Quantile-based modeling instead describes the full conditional distribution on the original time scale, but existing censored-data methods are either inflexible or produce logically inconsistent crossing quantile curves. We propose a Censored Non-crossing Quantile (CNQ) framework for right-censored data that jointly estimates several conditional survival quantiles and guarantees valid ordering by construction, with flexibility supplied by Kolmogorov-Arnold and Transformer backbones, and we establish a finite-sample excess-risk bound holding jointly across all fitted quantile levels. Across ...

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