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GEE analysis in joint mean-covariance model for longitudinal data

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  • Lu, Fei
  • Xue, Liugen
  • Cai, Xiong

Abstract

In this paper, we propose generalized estimating equations for the regression parameters in joint mean-covariance model for longitudinal data, motivated by the alternative Cholesky decomposition. This decomposition causes robust estimation of the correlation matrix against model misspecification for innovation variances.

Suggested Citation

  • Lu, Fei & Xue, Liugen & Cai, Xiong, 2020. "GEE analysis in joint mean-covariance model for longitudinal data," Statistics & Probability Letters, Elsevier, vol. 160(C).
    • Handle: RePEc:eee:stapro:v:160:y:2020:i:c:s0167715220300080
    DOI: 10.1016/j.spl.2020.108705
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    References listed on IDEAS

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    5. Weiping Zhang & Chenlei Leng & Cheng Yong Tang, 2015. "A joint modelling approach for longitudinal studies," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(1), pages 219-238, January.
    6. Mohsen Pourahmadi, 2007. "Cholesky Decompositions and Estimation of A Covariance Matrix: Orthogonality of Variance--Correlation Parameters," Biometrika, Biometrika Trust, vol. 94(4), pages 1006-1013.
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