Deep Nonparametric Inference for Conditional Hazard Function

发布时间：2023-11-29 作者：浏览次数：

Speaker:	苏雯	DateTime:	2023年12月4日（周一）上午10:00-11:00
Brief Introduction to Speaker: 苏雯博士在香港大学获得统计学博士学位。她在多伦多大学获得工业工程理学学士学位，在哥伦比亚大学获得生物统计学理学硕士学位。苏博士在生存分析方面的研究一直专注于开发新的统计方法，使用深度学习技术来分析复杂的事件发生时间数据，特别是在医学研究的背景下。		Place:	6号楼M415
Abstract:We propose a novel deep learning approach to nonparametric statistical inference for the conditional hazard function of survival time with right-censored data. We use a deep neural network (DNN) to approximate the logarithm of a conditional hazard function given covariates and obtain a DNN likelihood-based estimator of the conditional hazard function. Such an estimation approach grants model flexibility and hence relaxes structural and functional assumptions on conditional hazard or survival functions. We establish the consistency, convergence rate, and functional asymptotic normality of the proposed estimator. Subsequently, we develop new one-sample tests for goodness-of-fit evaluation and two-sample tests for treatment comparison. Both simulation studies and real application analysis show superior performances of the proposed estimators and tests in comparison with existing methods