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Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series
On the Control of False Discovery Rate in Multiple Testing
with no Assumption on Dependency
MB Rao
Center for Genome Information, Division of Epidemiology and Biostatistics,
Department of Environmental Health, and Department of Biomedical
Engineering, University of Cincinnati
3:30PM - Thursday, February 8, 2007
Room 170, Eighteenth Avenue Bldg. (EA 170)
ABSTRACT
Multiple hypotheses arise naturally in micro-array data and
epidemiology. There are essentially two components in a multiple testing
problem. 1. What constitutes as Type I error rate in the context of
multiple hypotheses testing? 2. Choice of a multiple testing procedure
controlling the error rate of your choice. A rudimentary introduction will
be provided covering both the components. Our choice of error rate is the
False Discovery Rate. Most multiple testing procedures available in the
literature controlling the False Discovery Rate assume either independence
or some specific type of dependence among the underlying statistics. We
propose a new sequential step-down procedure, which controls the false
discovery rate at the desired level no matter what the joint distribution
of the underlying statistics is. We use the optimization technique in
knapsack problems to demonstrate that the new procedure does the job
it is claimed to do. If time permits, I will spend some time on Closed
Testing Principle.
Meet the speaker in Room 212 Cockins Hall at 4:30
p.m. Refreshments will be served.
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