Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem

Add to your list(s) Download to your calendar using vCal

• James Scott, Duke University
• Friday 13 March 2009, 16:00-17:00
• MR12, CMS, Wilberforce Road, Cambridge, CB3 0WB.

If you have a question about this talk, please contact rbg24.

In this talk, I will present a theorem that characterizes a surprising discrepancy between fully Bayes and empirical-Bayes approaches to multiplicity adjustment in linear regression. This discrepancy arises from a different source than the failure to account for uncertainty in the empirical-Bayes estimate, which is the usual issue in such problems. Indeed, I will show that even at the extreme, when the empirical-Bayes estimate converges asymptotically to the true parameter value, the potential for a serious difference remains.

I will also highlight some interesting examples of Bayesian multiplicity adjustment on large data sets, with particular attention to a business application that involves large-scale screening of functional data.

This talk is part of the Statistics series.

This talk is included in these lists:

• All CMS events
• All Talks (aka the CURE list)
• CMS Events
• Cambridge Forum of Science and Humanities
• Cambridge Language Sciences
• Cambridge talks
• Chris Davis' list
• DPMMS Lists
• DPMMS info aggregator
• DPMMS lists
• Guy Emerson's list
• Hanchen DaDaDash
• Interested Talks
• MR12, CMS, Wilberforce Road, Cambridge, CB3 0WB
• Machine Learning
• School of Physical Sciences
• Statistical Laboratory info aggregator
• Statistics
• Statistics Group
• bld31
• custom
• rp587

Note that ex-directory lists are not shown.