中科院随机复杂结构与数据科学重点实验室

中科院数学与系统科学研究院

学术报告

题     目:   Large-Scale Two-Sample Inference for Sparse Means

报 告 人:  Prof. Tony Cai

                University of Pennsylvania                

时     间： 2015年12月23日（周三）  10:30-11:30

地    点：  数学院南楼613

摘     要： The conventional approach to 2-sample multiple testing is to first reduce the data matrix to a single vector of p-values and then choose a cutoff along the rankings to adjust for multiplicity. However, this inference framework often leads to suboptimal multiple testing procedures due to the loss of information in the data reduction step. In this talk, we introduce a new method to large-scale multiple testing for two sparse means. The problem is studied in a decision-theoretic framework and both oracle and data-driven procedures for FDR control are developed. The proposed oracle procedure employs a covariate-assisted ranking and screening (CARS) technique, and is shown to be optimal. A data-driven procedure is then developed to mimic the oracle procedure and its asymptotic properties are established. 

邀请人:  王启华