报告名称：Kernel quantile regression for semiparametric partially linear time-varying-coefficient model based on a history process of longitudinal data

报告人：刘秀芳 太原理工大学

报告时间：2026年3月11日 上午10:30--11:30

会议地点：伍卓群楼第一报告厅

校内联系人：李聪 [email protected]

报告摘要：This study delves into kernel quantile regression estimation for a semiparametric partially linear time-varying-coefficient model, which incorporates a history process with time-dependent covariates and a right-censored time-to-event variable. We propose a three-stage approach to construct the estimators of the parametric portion and nonparametric time-varying-coefficient function for this model, in view of inverse probability of censoring weighting (IPCW) technique. Additionally, we offer a procedure for variable selection among the time-dependent covariates in the parametric segment through the use of an adaptive LASSO penalty. The paper establishes the asymptotic normality of the proposed estimators and demonstrates that the penalized estimators possess the oracle property. A numerical simulation is implemented to evaluate the performance of the proposed estimators. Eventually, we apply the developed method to analyze medical cost data from a multicenter automatic defibrillator implantation trial (MADIT) to illustrate its practical utility.

个人简介：

刘秀芳，太原理工大学教师，博士毕业于吉林大学，2018年在加拿大里贾那大学访学一年。刘老师研究兴趣包括时间序列分析、生物统计等， 以第一作者或通讯作者在包括Journal of Multivariate Analysis, Statistical Methods in Medical Research 等国际知名统计期刊发表SCI论文12篇。