报告题目：Simultaneous variable selection and estimation for a partially linear Cox model
报 告 人： 胡涛 教授 首都师范大学
报告时间：2025年7月8日上午 10:30-11:30
报告地点：腾讯会议 ID：420-244-868
或点击链接直接加入会议：https://meeting.tencent.com/dm/pu864kdtYWLt
校内联系人：赵世舜 zhaoss@jlu.edu.cn
报告摘要：
We consider simultaneous variable selection and estimation for a deep neural network-based partially linear Cox model and propose a novel penalized approach. In particular, a two-step iterative algorithm is developed with the use of the minimum information criterion to ensure sparse estimation. The proposed method circumvents the curse of dimensionality while facilitating the interpretability of linear covariate effects on survival, and the algorithm greatly reduces the computational burden by avoiding the need to select the optimal tuning parameters that is usually required by many other popular penalties. The convergence rate and asymptotic properties of the resulting estimator are established along with the consistency of variable selection. The performance of the procedure is demonstrated through extensive simulation studies and an application to a myeloma dataset.
报告人简介 胡涛，首都师范大学数学科学学院教授，博士生导师。研究方向：生物统计、应用统计。在国内外学术刊物Journal of the American Statistical Association、 Biometrika、 Bioinformatics、 Biometrics、 Renewable Energy和《中国科学：数学》等上发表学术论文多篇。主持北京高校卓越青年科学家计划项目、国家自然科学基金面上项目、北京市自然科学基金重点研究专题等多个课题。
