ZAP: Z-Value Adaptive Procedures for False Discovery Rate Control With Side Information

The last statistics seminar of the semester will be a joint seminar with Business School. Please note the unusual time and location. The speaker will be spending time before and after the seminar on our school.

Title: ZAP: Z-Value Adaptive Procedures for False Discovery Rate Control With Side Information
Speaker: Dennis Leung , School of Mathematics and Statistics, University of Melbourne
Time and location: 11 am Friday 2 June 2023, Room 2020 Abercrombie Bldg H70 or on Zoom

Abstract: Adaptive multiple testing with covariates is an important research direction that has gained major attention in recent years, as it has been widely recognized that leveraging side information provided by auxiliary covariates can improve the power of testing procedures for controlling the false discovery rate (FDR), e.g. in the differential expression analysis of RNA-sequencing data, the average read depths across samples can provide useful side information alongside individual p-values, and incorporating such information promises to improve the power of existing methods. However, for two-sided hypotheses, the usual data processing step that transforms the primary statistics, generally known as z-values, into p-values not only leads to a loss of information carried by the main statistics but can also undermine the ability of the covariates to assist with the FDR inference. Motivated by this and building upon recent advances in FDR research, we develop ZAP, a z-value based covariate-adaptive methodology. It operates on the intact structural information encoded jointly by the z-values and covariates, to mimic an optimal oracle testing procedure that is unattainable in practice; the power gain of ZAP can be substantial in comparison with p-value based methods.

Short-bio : Dr. Dennis Leung completed his Ph.D. in statistics at the University of Washington in 2016 under the supervision of Professor Mathias Drton. After brief stints as postdoctoral fellows at the Chinese University of Hong Kong and the University of Southern California, he joined the School of Mathematics and Statistics at the University of Melbourne in 2019, shortly before the COVID pandemic.
His research in statistics revolves around hypothesis testing, within which there are two main streams of topics that he works on. The first is the development of new methodologies for multiple-hypothesis testing in different contexts, the topic of this talk. The second is developing limit theorems for test statistics, particularly those involving Studentization, to bridge the gap between theory and practice.