Abstract Details
Activity Number:
|
221
|
Type:
|
Topic Contributed
|
Date/Time:
|
Monday, August 4, 2014 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract #311357
|
View Presentation
|
Title:
|
Bayesian Modeling with Blockwise Hyper-G Priors
|
Author(s):
|
Agniva Som*+ and Christopher Hans and Steven N. MacEachern
|
Companies:
|
Ohio State University and Ohio State University and Ohio State University
|
Keywords:
|
g prior ;
conditional Lindley's paradox ;
information paradox ;
model selection ;
consistency ;
Gaussian hypergeometric function
|
Abstract:
|
Availability of closed form analytic representations of the Bayes factors under Zellner's g prior and its amelioration in the form of the mixtures of g priors helps to downscale the computational burden of inference in large problems and is a major reason for the widespread use of such priors. The hyper-g prior was formulated to address critical inconsistency issues associated with the original (fixed) g prior. Our investigations show that in spite of effectively dealing with the well-known paradoxes, the hyper-g prior still suffers from an alternative paradox when non-zero regression coefficients differ greatly in magnitude and basically collapses to a least squares solution in certain situations. In this article, we modify the ordinary g prior by proposing independent g priors on groups (or blocks) of predictor variables and investigate the theoretical properties of the new prior under a blockwise orthogonal design. We show that mixtures of blockwise g priors with carefully chosen blocks are capable of fixing the troubling irregularities associated with their g prior counterparts and also exhibit all the desirable properties of a sound modeling procedure.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development program, please contact the Education Department.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.