Training Survival Models using Scoring Rules

arXiv:2403.13150v1 Announce Type: new
Abstract: Survival Analysis provides critical insights for partially incomplete time-to-event data in various domains. It is also an important example of probabilistic machine learning. The probabilistic nature of the predictions can be exploited by using (proper) scoring rules in the model fitting process instead of likelihood-based optimization. Our proposal does so in a generic manner and can be used for a variety of model classes. We establish different parametric and non-parametric sub-frameworks that allow different degrees …

abstract analysis arxiv cs.ai cs.lg data domains event example insights likelihood machine machine learning nature optimization predictions process rules scoring stat.co stat.ml survival time-to-event data training type