报告时间：2026年6月24日（周三）下午 16:30-17:30
报告地点：苏州大学天赐庄校区博远楼208
报告人：桑培俊 副教授，加拿大滑铁卢大学
报告摘要：
Functional principal component analysis has been shown to be invaluable for revealing variation modes of longitudinal outcomes, which serves as important building blocks for forecasting and model building. Decades of research have advanced methods for functional principal component analysis often assuming independence between the observation times and longitudinal outcomes. Yet such assumptions are fragile in real-world settings where observation times may be driven by outcome-related reasons. Rather than ignoring the informative observation time process, we explicitly model the observational times by a general counting process dependent on time-varying prognostic factors. Identification of the mean, covariance function, and functional principal components ensues via inverse intensity weighting. We propose using weighted penalized splines for estimation and establish consistency and convergence rates for the weighted estimators. Simulation studies demonstrate that the proposed estimators are substantially more accurate than the existing ones in the presence of a correlation between the observation time process and the longitudinal outcome process. We further examine the finite-sample performance of the proposed method using the Acute Infection and Early Disease Research Program study.
报告人简介：
桑培俊，加拿大滑铁卢大学统计与精算系副教授。在Annals of Statistics, Biometrika, JASA, Journal of Machine Learning Research, Journal of Econometrics等杂志发表过多篇文章。主要研究方向是函数型数据和正样本和无标记数据的半监督学习（PU Learning），尤其是函数型数据分析回归模型中的统计推断问题以及PU Learning的半参模型。目前担任The American Statistician和Statistics and Computing的副主编。
邀请人：刘洋