Analysis of an EEG Experiment by a Multiple Testing Procedure

• Activity Number: 468 - Modern Topics in Hypothesis Testing
• Type: Contributed
• Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
• Sponsor: IMS
• Author(s): Shinjini Nandi* and Sanat Sarkar
• Companies: New York University, School of Medicine and Temple University
• Keywords: Generalized multi-way Grouped BH; Hierarchical Grouped BH; Simultaneous multi-way Grouped BH; Multiple Hypotheses Testing

Abstract

In this article, we propose a generalized multiple testing procedure, which is a weighted version of the well-known Benjamini-Hochberg (BH) procedure. The rigorous weighing scheme used by our method, enables it to encode structural information from simultaneous multi-way classification as well as hierarchical partitioning of hypotheses into groups, with provisions to accommodate overlapping groups. The method is proven to control the False Discovery Rate (FDR) when the p-values involved are Positively Regression Dependent on the Subset (PRDS) of null p-values and is shown to be more powerful than existing comparable multiple testing procedures. The corresponding data-adaptive version of the method is powerful and controls FDR under the assumption that the p-values involved are independent. We apply this procedure to an EEG dataset to exploit its complex spatio-temporal structure and analyze the impact of alcoholism on the human brain.