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:
|
599
|
Type:
|
Invited
|
Date/Time:
|
Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Statistical Learning and Data Mining
|
Abstract - #300066 |
Title:
|
Bayesian Inference for General Gaussian Graphical Models with Application to Multivariate Lattice Data
|
Author(s):
|
Adrian Dobra*+
|
Companies:
|
University of Washington
|
Address:
|
Box 354322, Seattle, WA, 98195-4322, USA
|
Keywords:
|
CAR model ;
Gaussian graphical model ;
G-Wishart distribution ;
Lattice data ;
Markov chain Monte Carlo (MCMC) simulation ;
Spatial statistics
|
Abstract:
|
We introduce efficient Markov chain Monte Carlo methods for inference and model determination in multivariate and matrix-variate Gaussian graphical models. Our framework is based on the G-Wishart prior for the precision matrix associated with graphs that can be decomposable or non-decomposable. We extend our sampling algorithms to a novel class of conditionally autoregressive models for sparse estimation in multivariate lattice data, with a special emphasis on the analysis of spatial data. These models embed a great deal of flexibility in estimating both the correlation structure across outcomes and the spatial correlation structure, thereby allowing for adaptive smoothing and spatial autocorrelation parameters. Our methods are illustrated using simulated and real-world examples, including an application to cancer mortality surveillance.
|
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.