报告题目：Orthogonalized Score Tests for ConditionalVariable Significance in Deep Partial Linear Cox Models

主讲人：郝美玲教授（对外经济贸易大学）

时间：2026年10月15日（周四）9:00 a.m.

形式：线上讲座

腾讯会议：199-328-319

主办单位：统计与数学学院

摘要：

Conditional statistical inference for high-dimensional survival data remains a fundamental yet challenging problem, particularly when the effects of nuisance variables are complex and difficult to specify parametrically. In this research, we propose a Deep Partial Linear Cox model that leverages neural networks to flexibly capture nonlinear nuisance effects while preserving interpretability for variables of primary interest. To test the conditional significance of high-dimensional variable sets within this framework, we develop an orthogonalized score test that effectively removes the influence of estimated nuisance components, thereby achieving valid inference in the presence of complex data dependencies. Our method accommodates settings where the number of tested parameters exceeds the sample size, without imposing sparsity assumptions. We establish the limiting null distribution of the proposed test statistic. Extensive numerical studies and an application to the TCGA breast cancer dataset demonstrate the superior performance and practical utility of our approach.

主讲人简介：

郝美玲，对外经济贸易大学教授，香港理工大学博士，玛格丽特公主癌症研究中心博士后。主要研究领域：高维数据分析，生物统计，非参数统计，强化学习。主持国家自然科学基金青年项目和面上项目各1项，学术论文发表于Journal of the American Statistic Association, Journal of Machine Learning Research, Statistica Sinica, Scandinavian Journal of Statistics，The Electronic Journal of Statistics, Computational Statistics and Data Analysis, Statistical Methods in Medical Research，Statistics in Medicine等期刊。