报告题目：Simultaneous variable selection and estimation for joint

models of longitudinal and failure time data with interval censoring

报告时间：2023年7月15日 上午8:30

报告地点：南湖校区教学科研楼104

主办单位：437必赢会员中心网页版

主讲人：易凤婷

易凤婷简介：云南大学437必赢会员中心网页版助理研究员，云南大学博士，云南大学与密苏里大学联合培养博士，西南财经大学博士后。曾获得中国博士后科学基金面上资助。主要从事纵向数据与区间删失数据联合模型及网络数据分析方面的研究。曾在CSDA及Biometrics上发表论文。

摘要：This paper discusses variable selection in the context of joint analysis of longitudinal data and failure time data. A large literature has been developed for either variable selection or the joint analysis but there exists only limited literature for variable selection in the context of the joint analysis when failure time data are right censored. Corresponding to this, we will consider the situation where instead of right-censored data, one observes interval-censored failure time data, a more general and commonly occurring form of failure time data. For the problem, a class of penalized likelihood-based procedures will be developed for simultaneous variable selection and estimation of relevant covariate effects for both longitudinal and failure time variables of interest. In particular, a Monte Carlo EM (MCEM) algorithm is presented for the implementation of the proposed approach. The proposed method allows for the number of covariates to be diverging with the sample size and is shown to have the oracle property. An extensive simulation study is conducted to assess the finite sample performance of the proposed approach and indicates that it works well in practical situations. An application is also provided.