报告人：吴静副教授（University of Rhode Island）

报告时间：2024年4月2日，上午10:30-11:10

报告地点：维格堂319

摘要：

When large amounts of survival data arrive in streams, conventional estimation methods become computationally infeasible since they require access to all observations at each accumulation point. We develop online updating methods for carrying out survival analysis under the Cox proportional hazards model in an online-update framework. Our methods are also applicable with time-dependent covariates. Specifically, we propose online-updating estimators as well as their standard errors for both the regression coefficients and the baseline hazard function. Extensive simulation studies are conducted to investigate the empirical performance of the proposed estimators. A large colon cancer data set from the Surveillance, Epidemiology, and End Results (SEER) program and a large venture capital (VC) data set with time-dependent covariates are analyzed to demonstrate the utility of the proposed methodologies.

个人简介：

Jing Wu is an associate professor and director of graduate studies in statistics at the University of Rhode Island. She received her BS in mathematics from Shanghai Jiao Tong University and her Ph.D. in statistics from the University of Connecticut. Her research interests primarily lie in missing data, big data, Bayesian statistics, survival and longitudinal data. She is currently an associate editor of Statistics and Its Interface and production editor of the New England Journal of Statistics in Data Science. Dr. Wu has published research articles in Journal of Computational and Graphical Statistics, Statistics in Medicine, Statistica Sinica, Technometrics, PNAS, JAMA Oncology, and Journal of Clinical Oncology, and so on.

邀请人：刘芳