Abstract Details
Activity Number:
|
433
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract - #310313 |
Title:
|
Bayesian Inference for Complex Survey Designs
|
Author(s):
|
Lane Burgette*+ and Terrance Savitsky
|
Companies:
|
RAND Corporation and RAND Corporation
|
Keywords:
|
Bayesian analysis ;
survey design ;
stratification
|
Abstract:
|
We propose a novel framework for fitting Bayesian models to data that were gathered according to a complex survey design. This method is generic in the sense that its implementation is essentially unaffected by the form of the outcome model, while still being fully Bayesian. Via simulation studies, we investigate the circumstances under which the method performs well, particularly in regard to small stratum sizes, and make comparisons to previous design- and model-based approaches. We also plan to investigate theoretical properties of the method, such as whether posterior consistency for a model with data gathered according to a simple random sample implies posterior consistency under this approach for handling complex survey designs.
|
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.