报告题目：Deep Partially Linear Cox Model for Current Status Data

报告时间：2023年5月6日 下午 16:00

报告地点：南湖校区教学科研楼307

主办单位：科研处/数学与统计学院

主讲人：童行伟

童行伟简介：北京师范大学教授，博士生导师，目前担任北京师范大学数理统计系系主任。博士毕业于北京大学数学科学学院，美国University of Missouri, Columbia 博士后，长期从事生物统计、金融统计、因果分析及稳健统计领域前沿研究。现担任中国概率统计学会的常务理事；中国现场统计研究会常务理事；高维数据统计分会秘书长；“应用概率统计”杂志的编委；资源与环境统计分会常务理事；国际生物统计学会（International）中国分会常务理事，北京大数据协会副会长等。主持科技部重点研发课题1项，和1项国家自然科学重点子课题、面上项目等6项，在Annals of Statistics, Biometrika, Statistica Sinica,等顶尖期刊发表50余篇 ，出版1本教材。

摘要：ABSTRACT Deep learning has continuously attained huge success in diverse fields, while its application to survival data analysis remains limited and deserves further exploration. For the analysis of current status data, a deep partially linear Cox model is proposed to circumvent the curse of dimensionality. Modeling flexibility is attained through using deep neural networks (DNNs) to accommodate nonlinear covariate effects and monotone splines to approximate the baseline cumulative hazard function. We establish the convergence rate of the proposed maximum likelihood estimators. Moreover, we derive that the finite-dimensional estimator for treatment covariate effects is √ n-consistent, asymptotically normal, and attains semiparametric efficiency. Finally, we demonstrate the performance of our procedures through extensive simulation studies and application to the real-world data on news popularity。