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Activity Number: 543
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #317371
Title: False Discovery Rate Control for Spatial Data
Author(s): Alexandra Chouldechova*
Companies: Carnegie Mellon University
Keywords: Multiple testing ; spatial data ; false discovery rate
Abstract:

In numerous problems arising in epidemiology, genetics, and imaging, large scale multiple testing is carried out in order to identify clusters or regions of interest. A typical approach entails using a multiple testing procedure to test each location for the presence of signal. Contiguous clusters of rejected hypotheses are then reported. We demonstrate that the location-wise error rate from the multiple testing procedure can greatly misrepresent the rate of erroneously reported clusters. We describe a method for estimating and controlling the cluster-wise false discovery rate in such spatial data settings. We also illustrate how to extend the method to incorporate various measures of cluster significance.


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