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Activity Number: 381 - Recent Advances in Multiple Testing and False Discovery Rate Analysis
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #300088 Presentation
Title: A New Approach for Large-Scale Multiple Testing with Application to FDR Control for Graphically Structured Hypotheses
Author(s): Wenge Guo* and Gavin Lynch and Joseph P. Romano
Companies: New Jersey Institute of Technology and Catchpoint Systems, Inc. and Stanford University
Keywords: multiple testing ; false discovery rate; per-family error rate; directed acyclic graph; dependence structure

In many large scale multiple testing applications, the hypotheses often have a known graphical structure, such as gene ontology in gene expression data. Exploiting this graphical structure in multiple testing procedures can improve power as well as aid in interpretation. In this talk, we will present a new general approach for large scale multiple testing, which can aid in developing new procedures under various settings with proven control of desired error rates. This approach is particularly useful for developing FDR controlling procedures, which is simplified as the problem of developing per-family error rate (PFER) controlling procedures. Specifically, for testing hypotheses with a directed acyclic graph (DAG) structure, by using the general approach, under the assumption of independence, we first develop a specific PFER controlling procedure and based on this procedure, then develop a new FDR controlling procedure, which can preserve the desired DAG structure among the rejected hypotheses. Through a small simulation study and a real data analysis, we illustrate nice performance of the proposed FDR controlling procedure for DAG-structured hypotheses.

Authors who are presenting talks have a * after their name.

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