讲座题目：Deep Partially Linear Cox Model for Current Status Data

主讲人：童行伟 教授 北京师范大学

讲座时间：2023年4月24日下午13:30-14:30

讲座地点：良乡校区数统楼311

主讲人简介：

童行伟，北京师范大学统计学院数理统计系主任，教授，博士生导师。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。主要从事生物统计、金融统计、因果分析及稳健统计领域前沿研究。

主讲内容：

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 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.

原文链接：https://stxy.btbu.edu.cn/xzkj/681f6ad7e6a44635b4652e5784a2ad99.htm