报告题目： High-Dimensional Robust Inference for Censored Linear Models

报告简介：Due to the directly statistical interpretation, censored linear regression offers a valuable complement to the Cox proportional hazards regression in survival analysis. We propose a rank-based high-dimensional inference for censored linear regression without imposing any moment condition on the model error. We develop theory of high-dimensional $U$-statistic, circumvent  challenges stemming from the non-smoothness of loss function, and establish convergence rate of regularized estimator and asymptotic normality of the resulting de-biased estimator as well as  consistency of the asymptotic variance estimation. As censoring can be viewed as a manner of trimming, it thereby strengthens the robustness of the rank-based high-dimensional inference, particularly for heavy-tailed model error or outlier in the presence of response. We evaluate the finite-sample performance of the proposed method via extensive simulation studies and demonstrate its utility by applying it to a subcohort study from The Cancer Genome Atlas (TCGA).

报告人：吴远山，现任中南财经政法大学统计与数学学院教授、博士生导师。主要从事大数据的统计理论基础、分位数回归、生存分析等相关的研究工作，在统计学期刊Journal of the American Statistical Association、Biometrika和人工智能期刊Journal of Machine Learning Research以及数学综合期刊SCIENCE CHINA Mathematics 等发表学术论文30余篇。目前担任ACM Transactions on Probabilistic Machine Learning 的Editorial Board Member。曾多次访问香港大学统计与精算学系、香港理工大学应用数学系等。

报告时间：2024年9月25日（星期三）上午9:00-12:00

报告地点：腾讯会议: 325-560-8777

主办单位：扬州大学数学科学学院

联系人：严钧

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