TY  - JOUR
PY  - 2006//
TI  - Sampling correlation matrices in Bayesian models with correlated latent variables
JO  - Journal of Computational and Graphical Statistics
A1  - Zhang, X.
A1  - Boscardin, W.J.
A1  - Belin, T.R.
SP  - 880
EP  - 896
VL  - 15
IS  - 4
N2  - 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>
LA  - en
SN  - 1061-8600
UR  - http://dx.doi.org/10.1198/106186006X160050
ID  - ref1
ER  -