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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, June 26, 2001}
\centerline{Sequoia Hall Rm. 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Bradley Efron}
\centerline{\sl Department of Statistics}
\centerline{\sl Stanford University}
\centerline{\sl Stanford,  CA 94305}
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\centerline{\bf Microarrays, Empirical Bayes Methods, and False Discovery 
Rates}

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In a classic two-sample problem we might for example  use Wilcoxon's
statistic to test for a difference between Treatment and Control subjects.
The analogous microarray experiment would yield thousands of Wilcoxon
statistics, one for each gene on the array, and we would be faced with a
difficult simultaneous inference situation. We will discuss two 
inferential
approaches to this problem: an empirical Bayes method that requires very
little a priori Bayesian modeling, and the frequentist method of "False
Discovery Rates" proposed by Benjamini and Hochberg in 1995. It turns out
that the two methods are very closely related and can be used together to
produce sensible simultaneous inferences.

(This is joint work with Rob Tibshirani and John Storey.)

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