Seminar Details

Seminar Details


Jan 29

3:30 pm

Prediction Error: Covariance Penalties and Cross-Validation

Bradley Efron


University of Washington - Department of Statistics

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