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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, October 1, 2002}
\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 Prediction error: covariance corrections and cross-validation}
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Suppose you have constructed a data-based estimation rule, for example a 
logistic regression model, and would like to know its performance as a 
predictor of future cases. There are two main theories concerning prediction 
error: (1) methods like Cp, AIC, and SURE (Stein's Unbiased Risk Estimator) 
that relate to the covariance between data points and the corresponding 
predicitons, and (2) Cross-validation, and related techniques such as the 
nonparametric boostrap. This talk concerns the relationship bwteen the two 
theories. Which method is preferable depends on the situation, at least a 
rough usage guide will be given.


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