University of Cambridge > Talks.cam > Statistics > Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

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

Download to your calendar using vCal

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

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:

Note that ex-directory lists are not shown.

Other lists

Faculty of Education, 184 Hills Road, Cambridge, CB2 8PQ, Room 2S3 Goldsmiths 1, Lecture Theatre, Department of Materials Science & Metallurgy Cockcroft Lecture Theatre, New Museums Site

Other talks

Talk title TBC Why people with schizophrenia die earlier? Mortality and accelerated ageing Defining and detecting discrimination: a new way forward Title tbc Online Causal Inference Semina Morning Tea and Coffee
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity