Citation

Zhang X, Boscardin WJ, Belin TR. Journal of Computational and Graphical Statistics 2006; 15(4): 880-896.

Copyright

(Copyright © 2006)

DOI

10.1198/106186006X160050

PMID

unavailable

Abstract

Hierarchical model specifications using latent variables are frequently used to reflect correlation structure in data. Motivated by the structure of a Bayesian multivariate probit model, we demonstrate a parameter-extended Metropolis-Hastings algorithm for sampling from the posterior distribution of a correlation matrix. Our sampling algorithms lead directly to two readily interpretable families of prior distributions for a correlation matrix. The methodology is illustrated through a simulation study and through an application with repeated binary outcomes on individuals from a study of a suicide prevention intervention. © 2006 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.

Language: en

Keywords

Metropolis-hastings algorithm; Multivariate probit model