报告题目：Robust approach for variable selection with high dimensional longitudinal data

报告人：付利亚 西安交通大学 副教授

照片：

报告时间：2020年8月27日17:00--18:00

报告平台：腾讯会议：258 751 923

邀请人：李本崇

报告摘要：This paper proposes a new robust smooth-threshold estimating equation to select important variables and automatically estimate parameters for high dimensional longitudinal data. Our proposed procedure works well when the number of covariates p increases as the number of subjects n increases and even when p exceeds n. A novel working correlation matrix is proposed to capture the correlation in the same subject. The proposed estimates are competitive with the those obtained with the true correlation structure especially when the data are contaminated. Moreover, the proposed method is robust against outliers existing in response variable and/or covariates. Furthermore, under some regularity conditions, the oracle properties are established for the proposed method under “large n, diverging p". Extensive simulation studies and a yeast cell cycle dataset are used to evaluate the performance of the proposed method, and results show that our proposed method is competitive with the existing robust variable selection procedures.

报告人简介：付利亚，西安交通大学伟德国际BETVlCTOR副教授、博士生导师。 2008.9-2010.9年澳大利亚联邦科工组织/昆士兰大学联合培养博士，2010年12月博士毕业，2011年昆士兰大学博士后，先后于2014.2-2015.8和2019.1-2019.8到昆士兰大学和昆士兰科技大学访问，在Biometrics，Statistics in Medicine，Computational Statistics and Data Analysis等发表SCI论文20余篇，出版教材一部。先后主持国家自然科学基金两项，教育部基金一项，陕西省基金一项，校级交叉项目一项。