Prof. Brad Efron : Large-Scale Prediction Problems

Posted by , part of the Statistics and Data Science Seminar.

At

Nov. 19, 2008, 4:15 p.m.

In

Lecture Center D2

Abstract

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.