arXiv:2510. 19374v2 Announce Type: replace-cross Abstract: We revisit Cox's proportional hazards model to improve variable selection in survival analysis.
arXiv:2606. 03689v1 Announce Type: cross Abstract: Survival Analysis (SA) is a statistical framework that models the time span until some event of interest occurs.
arXiv:2606. 04564v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have made rapid progress in standard classification and regression, but time-to-event survival prediction tasks have remained largely untouched.
arXiv:2602. 22179v2 Announce Type: replace Abstract: In many applications, it is important to identify subpopulations that survive longer or shorter than the rest of the population.
arXiv:2606. 06393v1 Announce Type: new Abstract: Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts.
arXiv:2212. 05260v4 Announce Type: replace-cross Abstract: Proper scoring rules encourage probabilistic predictions that match the true underlying distribution and are central to model evaluation, with increasing relevance in automated workflows such as AutoML.
Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts. However, in the presence of right censoring, the event time is only partially observed, rendering conventional scoring rules inapplicable in their standard form.
arXiv:2608. 04046v1 Announce Type: cross Abstract: Survival analysis is an established framework for analyzing time-to-event data, yet many clinical machine learning studies still binarize the outcome before model training.
The paper introduces a high‑dimensional censored MIDAS logistic regression for forecasting corporate distress, addressing right censoring, high‑dimensional mixed‑frequency predictors, and mixed‑frequency data. It uses inverse probability weighting for censoring, a sparse‑group penalty for estimation, and develops a de‑sparsified estimator with asymptotic theory that accounts for censoring and heavy tails. The method is validated via Monte Carlo simulations and applied to Chinese‑listed firms, with implementation available in the R package Survivalml.
arXiv:2606. 08305v1 Announce Type: cross Abstract: Externally controlled survival trials are increasingly used when concurrent randomized controls are infeasible, particularly in oncology and rare-disease settings with time-to-event endpoints.
Random Hazard Forests (RHF) is a survival tree ensemble that models how a patient's hazard changes over continuous time as new measurements arrive. RHF directly estimates a nonparametric hazard likelihood for predictable covariate processes, using an efficient working model to guide tree construction and then estimating flexible time‑varying hazards at each terminal node. By routing each tree based on the covariate state immediately before each time point, RHF can handle irregular and asynchronous covariate updates, and averaging across trees yields a pathwise hazard estimate that accurately captures changing risk in simulations and an intensive‑care application.
arXiv:2608. 00271v1 Announce Type: cross Abstract: A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.
arXiv:2606. 18281v1 Announce Type: cross Abstract: Conditional average treatment effects (CATEs) are central to treatment decision-making in personalized medicine.