报告时间：2021年12月13日 19：00-20：00

腾讯会议：620-606-835

报告人：李二倩（北方工业大学）

报告摘要：The proportional subdistribution hazard regression model has been widely used by clinical researchers for analyzing competing risks data. It is well known that quantile regression provides a more comprehensive alternative to model how covariates influence not only the location but also the entire conditional distribution. In this paper, we develop variable selection procedures based on penalized estimating equations for competing risks quantile regression. Asymptotic properties of the proposed estimators including consistency and oracle properties are established. Monte Carlo simulation studies are conducted, confirming that the proposed methods are efficient. A bone marrow transplant data set is analyzed to demonstrate our methodologies.

报告人介绍：李二倩，北方工业大学，讲师。本科毕业于中国科学技术大学，在中国人民大学统计学院获得博士学位。2019年入职北方工业大学理学院。在国内外期刊发表论文十余篇。主要研究领域为数理统计、统计推断、分位回归、竞争风险模型。

邀请人： 马学俊