Statistics and Data Science Seminar
Prof. Brad Efron
Stanford University
Large-Scale Prediction Problems
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.
Wednesday November 19, 2008 at 4:15 PM in Lecture Center D2