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Dynamic Conditionally Linear Mixed Models for Longitudinal Data

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  • M. Pourahmadi
  • M. J. Daniels

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  • M. Pourahmadi & M. J. Daniels, 2002. "Dynamic Conditionally Linear Mixed Models for Longitudinal Data," Biometrics, The International Biometric Society, vol. 58(1), pages 225-231, March.
    • Handle: RePEc:bla:biomet:v:58:y:2002:i:1:p:225-231
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.2002.00225.x
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    References listed on IDEAS

    as
    1. Michael J. Daniels & Robert E. Kass, 2001. "Shrinkage Estimators for Covariance Matrices," Biometrics, The International Biometric Society, vol. 57(4), pages 1173-1184, December.
    2. Lindsey, J. K., 1999. "Models for Repeated Measurements," OUP Catalogue, Oxford University Press, edition 2, number 9780198505594.
    3. Susan K. Mikulich & Gary O. Zerbe & Richard H. Jones & Thomas J. Crowley, 1999. "Relating the Classical Covariance Adjustment Techniques of Multivariate Growth Curve Models to Modern Univariate Mixed Effects Models," Biometrics, The International Biometric Society, vol. 55(3), pages 957-964, September.
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    Cited by:

    1. Daniels, Michael J., 2006. "Bayesian modeling of several covariance matrices and some results on propriety of the posterior for linear regression with correlated and/or heterogeneous errors," Journal of Multivariate Analysis, Elsevier, vol. 97(5), pages 1185-1207, May.
    2. Brajendra C. Sutradhar & Vandna Jowaheer & Gary Sneddon, 2008. "On a Unified Generalized Quasi–likelihood Approach for Familial–Longitudinal Non‐Stationary Count Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(4), pages 597-612, December.
    3. Lee, Keunbaik & Yoo, Jae Keun, 2014. "Bayesian Cholesky factor models in random effects covariance matrix for generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 111-116.
    4. Congdon, Peter, 2007. "Mixtures of spatial and unstructured effects for spatially discontinuous health outcomes," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3197-3212, March.
    5. Chen, Ziqi & Shi, Ning-Zhong & Gao, Wei & Tang, Man-Lai, 2011. "Efficient semiparametric estimation via Cholesky decomposition for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3344-3354, December.
    6. Mohsen Pourahmadi, 2002. "Graphical Diagnostics for Modeling Unstructured Covariance Matrices," International Statistical Review, International Statistical Institute, vol. 70(3), pages 395-417, December.
    7. Wang, Y. & Daniels, M.J., 2013. "Bayesian modeling of the dependence in longitudinal data via partial autocorrelations and marginal variances," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 130-140.
    8. Lee, Keunbaik & Lee, JungBok & Hagan, Joseph & Yoo, Jae Keun, 2012. "Modeling the random effects covariance matrix for generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1545-1551.

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