Bayesian analysis of covariance matrices and dynamic models for longitudinal data Academic Article

abstract

• Parsimonious modelling of the within-subject covariance structure while heeding its positive-definiteness is of great importance in the analysis of longitudinal data. Using the Cholesky decomposition and the ensuing unconstrained and statistically meaningful reparameterisation, we provide a convenient and intuitive framework for developing conditionally conjugate prior distributions for covariance matrices and show their connections with generalised inverse Wishart priors. Our priors offer many advantages with regard to elicitation, positive definiteness, computations using Gibbs sampling, shrinking covariances toward a particular structure with considerable flexibility, and modelling covariances using covariates. Bayesian estimation methods are developed and the results are compared using two simulation studies. These simulations suggest simpler and more suitable priors for the covariance structure of longitudinal data. 2002 Biometrika Trust.

authors

• Pourahmadi, Mohsen

published proceedings

• Biometrika

author list (cited authors)

• Daniels, M. J., & Pourahmadi, M.

citation count

• 139

complete list of authors

• Daniels, Michael J||Pourahmadi, Mohsen

publication date

• January 2002

publisher

• Oxford University Press (OUP)  Publisher

published in

• Biometrika  Journal

keywords

• Generic Health Relevance

Digital Object Identifier (DOI)

• 10.1093/biomet/89.3.553

start page

• 553

end page

• 566

volume

• 89

issue

• 3

URL

• http%3A%2F%2Fdx.doi.org%2F10.1093%2Fbiomet%2F89.3.553