title: Bayesian Inference for General Gaussian Graphical Models With Application to Multivariate Lattice Data.
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 a simulated example and a real-world application which concerns cancer mortality surveillance. Supplementary materials with computer code and the datasets needed to replicate our numerical results together with additional tables of results are available online.
authors.affiliation: Assistant Professor, Departments of Statistics, Biobehavioral Nursing, and Health Systems and the Center for Statistics and the Social Sciences, Box 354322, University of Washington, Seattle, WA 98195.
authors.forename: Adrian
authors.initials: A
authors.lastname: Dobra
authors.affiliation: Postdoctoral Research Fellow, Institut für Angewandte Mathematik, Universität Heidelberg, 69115 Heidelberg, Germany.
authors.forename: Alex
authors.initials: A
authors.lastname: Lenkoski
authors.affiliation: Assistant Professor, Department of Applied Mathematics and Statistics, University of California, Santa Cruz, CA 95064.
authors.forename: Abel
authors.initials: A
authors.lastname: Rodriguez
keywords: CAR model; G-Wishart distribution; Markov chain Monte Carlo (MCMC) simulation; Spatial statistics