报告题目：KERNEL MEETS SIEVE: TRANSFORMED HAZARDS MODELS WITH SPARSE LONGITUDINAL COVARIATES

报 告 人： 曹宏媛教授 佛罗里达州立大学

报告时间： 2026年3月9日 14：00-15：00

报告地点： 伍卓群楼第一报告厅

校内联系人：韩月才 [email protected]

报告摘要：We study the transformed hazards model with time-dependent covariates observed intermittently for the censored outcome. Existing work assumes the availability of the whole trajectory of the time-dependent covariates, which is unrealistic. We propose combining kernel-weighted log-likelihood and sieve maximum log-likelihood estimation to conduct statistical inference. The method is robust and easy to implement. We establish the asymptotic properties of the proposed estimator and contribute to a rigorous theoretical framework for general kernel-weighted sieve M-estimators. Numerical studies corroborate our theoretical results and show that the proposed method performs favorably over competing methods. The analysis of a data set from a COVID-19 study in Wuhan identifies clinical predictors that otherwise cannot be obtained using existing methods.

报告人简介：Hongyuan Cao is a professor of statistics at Florida State University. She got her Ph.D. from UNC-Chapel Hill. Her research interests include causal inference, multiple testing, survival analysis, and longitudinal data analysis. She is an elected fellow of ASA.