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Department of Statistics, The Ohio State University
Statistics and Biostatistics Colloquium Series
What is the probability of selecting the best t of k populations when k is extremely large?
Jason Wilson
University of California, Riverside
3:30PM - Tuesday, January 29, 2008
Room 240, Cockins Hall (CH 240)
ABSTRACT
In the 1950's Robert Bechhofer and Shanti S. Gupta laid the
foundations of what has become known as Ranking and Selection Methods
(RSM). The methods are useful for situations in which it is desired to
select from among k populations, as opposed to test for significance. The
area is highly developed for most of its problems when k is small (e.g. <
20). In this talk, we will explore the issue of selection when k becomes
extremely large (e.g. >1000). In particular, we will exhibit two new
selection rules that are suitable for such high dimensional problems. The
focus of the talk will be on on calculating the probability that such
selections are correct (PCS). Under realistic conditions, older selection
rules generally have PCS go to zero as k becomes large. However, under
our new rules useful probabilities can be found. Some results of a
simulation study will be presented which demonstrate the accuracy of
estimating PCS. An example using a real microarray data set will be
shown.
Meet the speaker in Room 212 Cockins Hall at 4:30
p.m. Refreshments will be served.
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