Abstract Details
Activity Number:
|
461
|
Type:
|
Invited
|
Date/Time:
|
Wednesday, August 7, 2013 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Statistics and the Environment
|
Abstract - #307409 |
Title:
|
Restricted Covariance Priors with Applications in Spatial Statistics
|
Author(s):
|
Adrian Dobra and Theresa Ruth Smith*+
|
Companies:
|
University of Washington and University of Washington
|
Keywords:
|
Gaussian graphical models ;
Markov chain Monte Carlo ;
disease mapping
|
Abstract:
|
We present a Bayesian model for area-level count data that uses Gaussian random effects with a novel type of G-Wishart prior on the inverse variance-covariance matrix. The usual G-Wishart prior restricts off-diagonal elements of the precision matrix to 0 according to the neighborhood structure of the study region. This preserves conditional independence of non-neighboring regions but is more flexible than the traditional intrinsic autoregression prior. One drawback of the usual G-Wishart prior is that it allows for both positive and negative associations between neighboring areas; whereas, most spatial priors induce only positive pairwise associations between the relative risks of neighboring areas. In this work we introduce a new type of G-Wishart distribution, which we call the negative G-Wishart distribution. This distribution only puts support over precision matrices that lead to positive associations. We illustrate Markov chain Monte Carlo sampling algorithms for the negative G-Wishart prior in a disease mapping context and compare our results to Bayesian hierarchical models based on intrinsic autoregression and semiparametric allocation models.
|
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.