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Bayesian Analysis of a Growth Curve Model with a General Autoregressive Covariance Structure

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  • J. C. Lee
  • C. H. Chang

Abstract

In this paper we consider from maximum likelihood and Bayesian points of view the generalized growth curve model when the covariance matrix has a Toeplitz structure. This covariance is a generalization of the AR(1) dependence structure. Inferences on the parameters as well as the future values are included. The results are illustrated with several real data sets.

Suggested Citation

  • J. C. Lee & C. H. Chang, 2000. "Bayesian Analysis of a Growth Curve Model with a General Autoregressive Covariance Structure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(4), pages 703-713, December.
    • Handle: RePEc:bla:scjsta:v:27:y:2000:i:4:p:703-713
    DOI: 10.1111/1467-9469.00217
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    Cited by:

    1. M. A. Alkhamisi & Ghazi Shukur, 2005. "Bayesian analysis of a linear mixed model with AR(p) errors via MCMC," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(7), pages 741-755.
    2. Dingjing Shi & Xin Tong, 2017. "The Impact of Prior Information on Bayesian Latent Basis Growth Model Estimation," SAGE Open, , vol. 7(3), pages 21582440177, August.
    3. Viroli, Cinzia, 2012. "On matrix-variate regression analysis," Journal of Multivariate Analysis, Elsevier, vol. 111(C), pages 296-309.

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