GSO Spring Speaker 2006

Correlation and Large-Scale Simultaneous Significance Testing

Bradley Efron
Max H. Stein Professor of Humanities and Sciences, Stanford University Joint with Research Colloquium

Start Date and Time: Thu, 9 Mar 2006, 4:30 PM

End Date and Time: Thu, 9 Mar 2006, 6:00 PM

Venue: MATH 175


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.

Last Updated: Sep 18, 2017 5:24 PM

Purdue Department of Statistics, 250 N. University St, West Lafayette, IN 47907

Phone: (765) 494-6030, Fax: (765) 494-0558

© 2018 Purdue University | An equal access/equal opportunity university | Copyright Complaints

Trouble with this page? Disability-related accessibility issue? Please contact the College of Science Webmaster.