报告题目：Neural frailty machine: beyond proportional hazards assumption in neural survival regressions
报 告 人：郁文 教授 复旦大学
报告时间：2022年7月11日 14：00-15：00
报告地点：腾讯会议 ID：367231583 会议密码：0711
校内联系人：王培洁 wangpeijie@jlu.edu.cn
报告摘要：The Cox proportional hazards model is the most widely used regression model for survival data with right censoring. When the proportional hazards assumption does not hold, many alternative semiparametric models are proposed, including additive hazards models, accelerated failure time models, and linear transformation models, etc. Another way to extend the Cox model is to introduce frailty into the hazard function. Meanwhile, with recent development in machine learning, neural networks with deep structures are incorporated into the survival models to extend the linear structure. This brings great flexibility for survival data modeling. We consider a class of nonparametric hazard models with frailty and use neural networks to fit the model, which is called neural frailty machine. The likelihood function for right censoring is used to be the objective function. Two strategies are considered. One is to combine the non-parametric MLE and the neural networks and the other is to use the neural networks for all the function approximations. We show that the proposed estimators are consistent. In the experiments, we find out that the second strategy gives out better prediction performances more often, especially for data with large sizes.
报告人简介：郁文，复旦大学管理学院统计与数据科学系教授、博士生导师、系主任。主要从事生存分析、半参数模型、两阶段抽样设计、半监督推断等研究，在国内外学术期刊发表论文三十篇，主持多项国家自然科学基金、教育部博士点基金研究工作。担任中国现场统计研究会、全国工业统计学教学研究会、上海市质量技术应用统计学会理事。
