Seminar Details

Seminar Details


Feb 2

3:30 pm

Large-Scale Prediction Problems

Bradley Efron


Stanford University - Professor of Statistics & Biostatistics

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 >> n situations. It is closely related to the Shrunken Centroids algorithm of Tibshirani, Hastie, Narasimhan, and Chu.