@article{ref1,
title="Sampling correlation matrices in Bayesian models with correlated latent variables",
journal="Journal of Computational and Graphical Statistics",
year="2006",
author="Zhang, X. and Boscardin, W.J. and Belin, T.R.",
volume="15",
number="4",
pages="880-896",
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.<p /><p>Language: en</p>",
language="en",
issn="1061-8600",
doi="10.1198/106186006X160050",
url="http://dx.doi.org/10.1198/106186006X160050"
}