To Join via Zoom: To join this seminar virtually, please request Zoom connection details from headsec [at] stat.ubc.ca
Abstract: Analyzing and interpreting high-dimensional data in applications such as genomics and neuroimaging often requires the simultaneous testing of a large number of hypotheses. In such multiple testing situations, the state-of-the-art approach selects one subset of hypotheses whose False Discovery Rate (FDR) in controlled. The FDR is the expected proportion of false positives (FDP) among selected hypotheses.
In contrast, post hoc inference aims to build confidence bounds on the FDP contained in arbitrary subsets of hypotheses, leading to improvements in interpretability and reproducibility of the results. We show how to construct and efficiently implement post hoc bounds that are adaptive to unknown dependence. We illustrate their application to differential gene expression studies in genomics, and fMRI studies in neuroimaging.