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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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  %Time of talk, Weekday, Date of talk\\
  4:15 p.m., Tuesday, April 1, 2008\\
  Sequoia Hall Room 200\\
  (Cookies at 3:45 in 1st Floor Lounge)
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  \textsl{Susan A. Murphy} \\
  Department of Statistics\\
  University of Michigan
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  \subsection*{A Prediction Interval for the Misclassification Rate and Value}
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The misclassification rate of a classifier and the value of a policy
are non-smooth functions of the classifier and policy respectively.
Both suffer from bias due to over-fitting and while the
misclassification rate is a "minimized" quantity the value of a policy
is a "maximized" quantity.  For all of these reasons the construction
of measures of confidence such as estimates of variance and
confidence/prediction intervals are challenging.   We discuss how these
problems are similar and propose a method based on the use of a smooth
upper bound combined with the bootstrap.  This upper bound utilizes the
surrogate loss that is used in the construction of the classifier (or
policy).
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