Non-Crossing Deep Quantile Regression for Distributional Survival Prediction

• URL: http://arxiv.org/abs/2608.16864v1
• Date: Mon, 17 Aug 2026 17:46:41 GMT
• Title: Non-Crossing Deep Quantile Regression for Distributional Survival Prediction
• Abstract summary: Quantile-based modeling instead describes the full conditional distribution on the original time scale.<n>We propose a Censored Non-crossing Quantile (CNQ) framework for right-censored data.<n>It jointly estimates several conditional survival quantiles and guarantees valid ordering by construction.
• Score: 27.498297952127533
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 27 simulation settings and six cohorts the framework attains lower pinball loss than quantile-, hazard- and tree-based competitors whenever the conditional distribution is asymmetric, with interval coverage closer to nominal on all six. In two clinical case studies (METABRIC, breast cancer; FLCHAIN, population mortality) it recovers covariate effects that vary across the survival distribution and would be hidden by a single hazard ratio, and yields coherent individualized quantile milestones. Code: https://github.com/BIG-S2/deepcnq

Related papers

• Conditional Diffusion for Nonparametric Instrumental Variable Quantile Regression [18.254578337569264]
  This work proposes a two-stage estimator that combines conditional diffusion modeling with a kernel-parametriced conditional moment formulation.<n>We establish an excess-risk bound for the proposed estimator and derive end-to-end total variation guarantees for the conditional diffusion model.<n> Simulation studies and a real-data application demonstrate that the proposed method outperforms existing nonparametric IVQR approaches.
  arXiv  Detail & Related papers  (2026-08-08T16:01:24Z)
• Quantile Geometry Regularization for Distributional Reinforcement Learning [20.41658533815372]
  We propose Robust Quantile-based Implicit Quantile Networks (RQIQN)<n>We first reinterpret a snapshot of IQN loss as a collection of local empirical quantile estimation problems over sampled current fractions.<n>We then robustify each local slot with a Wasserstein distributionally robust quantile estimation formulation, yielding a closed-form, fraction-dependent correction to the Bellman target.
  arXiv  Detail & Related papers  (2026-05-05T09:38:34Z)
• On the Probability of First Success in Differential Evolution: Hazard Identities and Tail Bounds [0.0]
  We study first-hitting times in Differential Evolution (DE) through a conditional hazard frame work.<n>For the L-SHADE algorithm with current-to-$p$best/1 mutation, we construct a checkable algorithmic witness event $mathcal L_t$ under which the conditional hazard admits an explicit lower bound.
  arXiv  Detail & Related papers  (2026-01-16T18:24:24Z)
• Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression [102.24287051757469]
  We study self-supervised covariance estimation in deep heteroscedastic regression.<n>We derive an upper bound on the 2-Wasserstein distance between normal distributions.<n>Experiments over a wide range of synthetic and real datasets demonstrate that the proposed 2-Wasserstein bound coupled with pseudo label annotations results in a computationally cheaper yet accurate deep heteroscedastic regression.
  arXiv  Detail & Related papers  (2025-02-14T22:37:11Z)
• Semiparametric conformal prediction [79.6147286161434]
  We construct a conformal prediction set accounting for the joint correlation structure of the vector-valued non-conformity scores.<n>We flexibly estimate the joint cumulative distribution function (CDF) of the scores.<n>Our method yields desired coverage and competitive efficiency on a range of real-world regression problems.
  arXiv  Detail & Related papers  (2024-11-04T14:29:02Z)
• Risk and cross validation in ridge regression with correlated samples [72.59731158970894]
  We provide training examples for the in- and out-of-sample risks of ridge regression when the data points have arbitrary correlations.<n>We demonstrate that in this setting, the generalized cross validation estimator (GCV) fails to correctly predict the out-of-sample risk.<n>We further extend our analysis to the case where the test point has nontrivial correlations with the training set, a setting often encountered in time series forecasting.
  arXiv  Detail & Related papers  (2024-08-08T17:27:29Z)
• Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise [51.87307904567702]
  Quantile regression is a leading approach for obtaining such intervals via the empirical estimation of quantiles in the distribution of outputs.<n>We propose Relaxed Quantile Regression (RQR), a direct alternative to quantile regression based interval construction that removes this arbitrary constraint.<n>We demonstrate that this added flexibility results in intervals with an improvement in desirable qualities.
  arXiv  Detail & Related papers  (2024-06-05T13:36:38Z)
• Inference for High Dimensional Censored Quantile Regression [8.993036560782137]
  This paper proposes a novel procedure to draw inference on all predictors within the framework of global censored quantile regression. We show that our procedure can properly quantify the uncertainty of the estimates in high dimensional settings. We apply our method to analyze the heterogeneous effects of SNPs residing in lung cancer pathways on patients' survival.
  arXiv  Detail & Related papers  (2021-07-22T23:57:06Z)
• Understanding the Under-Coverage Bias in Uncertainty Estimation [58.03725169462616]
  quantile regression tends to emphunder-cover than the desired coverage level in reality. We prove that quantile regression suffers from an inherent under-coverage bias. Our theory reveals that this under-coverage bias stems from a certain high-dimensional parameter estimation error.
  arXiv  Detail & Related papers  (2021-06-10T06:11:55Z)
• Improving Conditional Coverage via Orthogonal Quantile Regression [12.826754199680472]
  We develop a method to generate prediction intervals that have a user-specified coverage level across all regions of feature-space. We modify the loss function to promote independence between the size of the intervals and the indicator of a miscoverage event. We empirically show that the modified loss function leads to improved conditional coverage, as evaluated by several metrics.
  arXiv  Detail & Related papers  (2021-06-01T11:02:29Z)
• Tracking disease outbreaks from sparse data with Bayesian inference [55.82986443159948]
  The COVID-19 pandemic provides new motivation for estimating the empirical rate of transmission during an outbreak. Standard methods struggle to accommodate the partial observability and sparse data common at finer scales. We propose a Bayesian framework which accommodates partial observability in a principled manner.
  arXiv  Detail & Related papers  (2020-09-12T20:37:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.