AI RESEARCH
Flexible Deep Neural Networks for Partially Linear Survival Data: Estimation and Survival Inference
arXiv CS.LG
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ArXi:2512.10570v2 Announce Type: replace-cross We propose a flexible deep neural network (DNN) framework for modeling survival data within a partially linear regression structure. The approach preserves interpretability through a parametric linear component for covariates of primary interest, while a nonparametric DNN component captures complex time-covariate interactions among nuisance variables. We refer to the method as FLEXI-Haz, a FLEXIble Hazard model with a partially linear structure.