JSM 2011 Online Program

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

Abstract Details

Activity Number: 123
Type: Contributed
Date/Time: Monday, August 1, 2011 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #303094
Title: Multiset Model Selection
Author(s): Dipayan Maiti*+ and Scotland Charles Leman
Companies: Virginia Tech and Virginia Tech
Address: Department of Statistics, Blacksburg, VA, 24060, United States
Keywords: Multiset ; Bayesian Model Selection ; Bayesian Model Averaging
Abstract:

The Multiset Sampler has previously been deployed and developed for e?cient sampling from complex stochastic processes. We extend the sampler and the surrounding theory to high dimensional model selection problems. In such problems e?cient exploration of the model space becomes a challenge since independent and ad-hoc proposals might not be able to jointly propose multiple parameter sets which correctly explain a new proposed model. In order to overcome this we propose a multiset on the model space to enable e?cient exploration of multiple model modes. The model selection framework is based on independent priors for the parameters and model indicators on variables. While under this method we do not obtain typical Bayesian model averaged estimates for the parameters, we show that the multiset model averaged parameter estimate is a mixture a distribution from which the true Bayesian model probabilities and the model averaged parameter estimate can be obtained.


The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2011 program




2011 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Continuing Education program, please contact the Education Department.