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

Mathematics – Statistics Theory

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Published in at http://dx.doi.org/10.1214/10-AOS792 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of

Scientific paper

10.1214/10-AOS792

This paper studies the multiplicity-correction effect of standard Bayesian variable-selection priors in linear regression. Our first goal is to clarify when, and how, multiplicity correction happens automatically in Bayesian analysis, and to distinguish this correction from the Bayesian Ockham's-razor effect. Our second goal is to contrast empirical-Bayes and fully Bayesian approaches to variable selection through examples, theoretical results and simulations. Considerable differences between the two approaches are found. In particular, we prove a theorem that characterizes a surprising aymptotic discrepancy between fully Bayes and empirical Bayes. This discrepancy arises from a different source than the failure to account for hyperparameter uncertainty in the empirical-Bayes estimate. Indeed, even at the extreme, when the empirical-Bayes estimate converges asymptotically to the true variable-inclusion probability, the potential for a serious difference remains.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-613828

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.