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
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- LinkedLinked via arxiv author · 85%Shuai Huang →
“Non-Crossing Deep Quantile Regression for Distributional Survival Prediction”
- LinkedLinked via arxiv author · 85%Zhe Qu →
“Non-Crossing Deep Quantile Regression for Distributional Survival Prediction”
- LinkedLinked via arxiv author · 85%Zhaowei Hua →
“Non-Crossing Deep Quantile Regression for Distributional Survival Prediction”
- LinkedLinked via arxiv author · 85%Guohao Shen →
“Non-Crossing Deep Quantile Regression for Distributional Survival Prediction”
- LinkedLinked via arxiv author · 85%Rui Tang →
“Non-Crossing Deep Quantile Regression for Distributional Survival Prediction”
- LinkedLinked via arxiv author · 85%Hongtu Zhu →
“Non-Crossing Deep Quantile Regression for Distributional Survival Prediction”