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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, May 13, 2003}
\centerline{Sequoia Hall Room 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}
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\centerline{\bf Large-Scale Simultaneous Hypothesis Testing}
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Abstract:

     "Multiple comparisons" used to mean doing 2 or 3 or 10 hypothesis
tests at the same time. Now statisticians need to consider 500 or 5000
testing situations simultaneously, in microarray or frmi analyses for
example. New problems and new opportunities arise in these contexts.
I will discuss an empirical Bayes approach, related to false discovery
rates, focusing on the choice of an appropriate null hypothesis for
large-scale hypothesis testing. Two genomics examples will be used to
motivate the methodology.

        This is joint work with Rob Tibshirani.


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