Flexible Deep Neural Networks for Partially Linear Survival Data: Estimation and Survival Inference
Factual evidence
What the source reports
Researchers propose FLEXI-Haz, a deep neural network for survival data with a partially linear structure, combining interpretability with complex time-covariate interactions.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 21 October 2026
- Collected by OneBench
- 28 Apr 2026, 07:59 UK
Stored source excerpt
arXiv:2512.10570v2 Announce Type: replace-cross Abstract: We propose a flexible deep neural network (DNN) framework for modeling survival data within a partially linear regression structure. The…
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OneBench interpretation
Institutional assessment
So what
This research outlines a framework for survival models that balances deep learning's predictive power with a transparent linear component, directly addressing regulatory demands for explainability in critical financial models.
Do what
This approach offers a pathway to potentially deploy more complex survival models in regulated environments by embedding an interpretable linear component, impacting future model development strategies.