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