Speaker: Dennis Leung
Affiliation: University of Melbourne


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 false discovery rate 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.

About Statistics, modelling and operations research seminars

Students, staff and visitors to UQ are welcome to attend our regular seminars.

The events are jointly run by our Operations research and Statistics and probability research groups.

The Statistics, modelling and operations research (SMOR) Seminar series seeks to celebrate and disseminate research and developments across the broad spectrum of quantitative sciences. The SMOR series provides a platform for communication of both theoretical and practical developments, as well as interdisciplinary topics relating to applied mathematics and statistics.


Physiology Building (63)
Room: 348 (and via Zoom: