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  \textbf{\Large{\textsc{STANFORD UNIVERSITY}}}\\[5pt]
  \textbf{\Large{\textsc{DEPARTMENT OF STATISTICS}}}\\[5pt]
  \Large{\textsc{DEPARTMENTAL SEMINAR}}
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  4:15 p.m., Tuesday, July 1, 2008\\
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  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Bradley Efron} \\
  Department of Statistics\\
  Stanford University
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  \subsection*{LARGE-SCALE PREDICTION PROBLEMS}
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\noindent

Classical prediction methods such as Fisher's linear discriminant
function were designed for small-scale problems, where the number N
of candidate predictors was much smaller than the number of observations
n. Modern scientific devices often reverse this situation. A micro-
array analysis, for example, might include $n=100$ subjects measured
on N=10,000 genes, each of which is a potential predictor. I will
discuss ``Ebay'', an empirical Bayes prediction algorithm designed to
handle $N  \gg n$ situations.  It is closely related to the Shrunken
Centroids algorithm of Tibshirani, Hastie, Narasimhan, and Chu.


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