Report Title：KERNEL MEETS SIEVE: TRANSFORMED HAZARDS MODELS WITH SPARSE LONGITUDINAL COVARIATES
Reporter：Professor Cao Hongyuan
Affiliation: Florida State University
Report Time:14:00-15:00 March 9,2026
Report Location: First Lecture Hall, Wu Zhuoqun Building
University Contact: Han Yuecai (hanyc@jlu.edu.cn)
Abstract:
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.
Bio:
Cao Hongyuan 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.
