GSO Spring Speaker 2006
Correlation and Large-Scale Simultaneous Significance Testing
Bradley Efron
Professor
Max H. Stein Professor of Humanities and Sciences, Stanford University Joint with Research Colloquium
Venue: MATH 175
Abstract:
Large-scale hypothesis testing problems, with hundreds or thousands of test statistics "z[i]" to consider at once, have become commonplace in current practice. Applications of popular analysis methods such as false discovery rates do not require independence of the z[i]'s but their accuracy can be compromised in high-correlation situations. This talk discusses methods, both theoretical and computational, for assessing the size and effect of correlation in large-scale testing problems. A microarray example will be used to illustrate the ideas. The example shows surprisingly large correlations that badly destabilize standard statistical analyses, but newer methods can remedy at least part of the trouble.