报告题目：Deep Generative Estimation of Conditional Survival Function

报告时间：2023-04-12  15:30-17:00

报告人：赵兴球 教授(香港理工大学)

报告地点：理学院东北楼209报告厅

报告摘要：Censored Survival data are commonly encountered in modern medicine, econometrics and social science. Its unique characteristics pose significant challenges to the analysis of such data and the existing methods often suffer grave consequences when the underlying model is misspecified. To address these difficulties, we propose a model-free two-stage generative approach for estimating the conditional survival function given predictors. We first learn a conditional generator nonparametrically for the joint conditional distribution of observed variables, and then construct the nonparametric maximum likelihood estimators of conditional distribution functions based on samples from the conditional generator. Subsequently, we study the convergence properties of the proposed estimator and establish its consistency. Simulation studies under various settings show the superior performance of the deep conditional generative approach over the classical modeling approaches and applications to real data yields reasonable predictions.