Statistics and Computing

, Volume 23, Issue 1, pp 109–122 | Cite as

Bayesian tests on components of the compound symmetry covariance matrix

Open Access


Complex dependency structures are often conditionally modeled, where random effects parameters are used to specify the natural heterogeneity in the population. When interest is focused on the dependency structure, inferences can be made from a complex covariance matrix using a marginal modeling approach. In this marginal modeling framework, testing covariance parameters is not a boundary problem. Bayesian tests on covariance parameter(s) of the compound symmetry structure are proposed assuming multivariate normally distributed observations. Innovative proper prior distributions are introduced for the covariance components such that the positive definiteness of the (compound symmetry) covariance matrix is ensured. Furthermore, it is shown that the proposed priors on the covariance parameters lead to a balanced Bayes factor, in case of testing an inequality constrained hypothesis. As an illustration, the proposed Bayes factor is used for testing (non-)invariant intra-class correlations across different group types (public and Catholic schools), using the 1982 High School and Beyond survey data.


Bayes factor Covariance matrices Gibbs Sampler Intra-class correlation Compound symmetry Savage-Dickey density ratio 


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Copyright information

© The Author(s) 2011

Authors and Affiliations

  1. 1.Department of Methodology and StatisticsTilburg UniversityTilburgThe Netherlands
  2. 2.Department of Research Methodology, Measurement and Data AnalysisUniversity of TwenteEnschedeThe Netherlands

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