Abstract Details
|
Activity Number:
|
364
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Section on Bayesian Statistical Science
|
| Abstract - #310356 |
|
Title:
|
A Note on DIC Justification
|
|
Author(s):
|
Shouhao Zhou*+
|
|
Companies:
|
The University of Texas MD Anderson Cancer Center
|
|
Keywords:
|
bayesian modeling ;
model selection ;
deviance ;
DIC
|
|
Abstract:
|
DIC (Deviance Information Criterion) has been widely used in applications of Bayesian modelling. As a Bayesian adaption of AIC, it heuristically estimates the out-of-sample plug-in log-likelihood, a relative risk based on Kullback-Leibler divergence, but the estimation itself is criticized for lack of a clear theoretical foundation. In this article, I will provide a rigid justification for DIC, explore its generalization for mis-specified candidate models, and discuss the switch to a modified version of DIC with respect to Bayesian expected loss.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 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.
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.