报告题目： Semiparametric Inference for the Functional Cox Model

报告人：郝美玲副教授

报告摘要：This article studies penalized semiparametric maximum partial likelihood estimation and hypothesis testing for the functional Cox model in analyzing right-censored data with both functional and scalar predictors. Deriving the asymptotic joint distribution of finite-dimensional and infinite-dimensional estimators is a very challenging theoretical problem due to the complexity of semiparametric models. For the problem, we construct the Sobolev space equipped with a special inner product and discover a new joint Bahadur representation of estimators of the unknown slope function and coefficients. Using this key tool, we establish the asymptotic joint normality of the proposed estimators and the weak convergence of the estimated slope function, and then construct local and global confidence intervals for an unknown slope function. Furthermore, we study a penalized partial likelihood ratio test, show that the test statistic enjoys the Wilks phenomenon, and also verify the optimality of the test. The theoretical results are examined through simulation studies, and a right-censored data example from the Improving Care of Acute Lung Injury Patients study is provided for illustration. Supplementary materials for this article are available online.

报告时间：4月7日（周五）上午10:10—11:10

报告地点：统计学院213会议室

报告人简介： 郝美玲，现为对外经济贸易大学副教授。郝美玲副教授是香港理工大学博士，多伦多玛格丽特公主癌症研究中心博士后。郝美玲博士一直从事生存分析、高维数据分析、生物统计，非参数统计，生物信息工程等方面的理论和应用研究，在Journal of the American Statistical Association，Journal of Machine Learning Research，Statistica Sinica，Genetic Epidemiology等国际知名学术期刊上发表论文近20篇。