Non-Crossing Deep Quantile Regression for Distributional Survival Prediction
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...
| Authors | Shuai Huang, Zhe Qu, Zhaowei Hua et al. |
|---|---|
| Primary category | stat.ML |
| Published | 17 August 2026 |
| Last updated at source | 17 August 2026 |
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