报告题目：Model selection for longitudinal data
报 告 人：付利亚 副教授（西安交通大学）
报告时间：2022年12月16日（星期五）10:00-11:00
报告地点：腾讯会议（ID：557 649 298）
校内联系人：牛一 副教授 联系电话：84708351-8081
报告摘要:In this talk, I will talk about the model selection for longitudinal data. Firstly, I will present two new criteria for selecting the best correlation matrix among the candidates with any arbitrary structures, even for irregularly timed measurements. The simulation results demonstrate that the new criteria perform more similarly to EAIC and EBIC as the sample size becomes large. However, their performance is much enhanced when the sample size is small and the number of measurements is large. Secondly, I will talk about a new robust smooth-threshold estimating equation to select important variables and automatically estimate parameters for high dimensional longitudinal data. A novel working correlation matrix is proposed to capture correlations within the same subject. The proposed procedure works well when the number of covariates p increases as the number of subjects n increases. The proposed estimates are competitive with the estimates obtained with the true correlation structure, especially when the data are contaminated. Moreover, the proposed method is robust against outliers in the response variables and/or covariates. Furthermore, the oracle properties for robust smooth-threshold estimating equations under large n and diverging p are established under some regularity conditions. Extensive simulation studies and a yeast cell cycle data are used to evaluate the performance of the proposed method, and results show that the proposed method is competitive with existing robust variable selection procedures.
报告人简介：付利亚，西安交通大学副教授、博士生导师，2010年12月博士毕业，2008年-2019年期间，先后在澳大利亚联邦科工组织、昆士兰大学和昆士兰科技大学学习和访问。在Biometrics，Statistics in Medicine，Journal of Multivariate Analysis等期刊发表SCI论文30篇，出版关于纵向数据分析书籍两部，主持国家和省部级项目4项目。
