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Joint estimation of mean-covariance model for longitudinal data with basis function approximations


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  • Mao, Jie
  • Zhu, Zhongyi
  • Fung, Wing K.
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    When the selected parametric model for the covariance structure is far from the true one, the corresponding covariance estimator could have considerable bias. To balance the variability and bias of the covariance estimator, we employ a nonparametric method. In addition, as different mean structures may lead to different estimators of the covariance matrix, we choose a semiparametric model for the mean so as to provide a stable estimate of the covariance matrix. Based on the modified Cholesky decomposition of the covariance matrix, we construct the joint mean-covariance model by modeling the smooth functions using the spline method and estimate the associated parameters using the maximum likelihood approach. A simulation study and a real data analysis are conducted to illustrate the proposed approach and demonstrate the flexibility of the suggested model.

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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 55 (2011)
    Issue (Month): 2 (February)
    Pages: 983-992

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    Handle: RePEc:eee:csdana:v:55:y:2011:i:2:p:983-992

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    Keywords: Basis function BIC B-splines Modified Cholesky decomposition Partially linear model;


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    1. He, Xuming & Fung, Wing K. & Zhu, Zhongyi, 2005. "Robust Estimation in Generalized Partial Linear Models for Clustered Data," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 100, pages 1176-1184, December.
    2. Xuming He, 2002. "Estimation in a semiparametric model for longitudinal data with unspecified dependence structure," Biometrika, Biometrika Trust, Biometrika Trust, vol. 89(3), pages 579-590, August.
    3. Wei Biao Wu, 2003. "Nonparametric estimation of large covariance matrices of longitudinal data," Biometrika, Biometrika Trust, Biometrika Trust, vol. 90(4), pages 831-844, December.
    4. Jianqing Fan & Runze Li, 2004. "New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 99, pages 710-723, January.
    5. Zhongyi Zhu & Wing K. Fung & Xuming He, 2008. "On the asymptotics of marginal regression splines with longitudinal data," Biometrika, Biometrika Trust, Biometrika Trust, vol. 95(4), pages 907-917.
    6. Fan, Jianqing & Huang, Tao & Li, Runze, 2007. "Analysis of Longitudinal Data With Semiparametric Estimation of Covariance Function," Journal of the American Statistical Association, American Statistical Association, American Statistical Association, vol. 102, pages 632-641, June.
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    Cited by:
    1. Li, Yang & Zhao, Hui & Sun, Jianguo & Kim, KyungMann, 2014. "Nonparametric tests for panel count data with unequal observation processes," Computational Statistics & Data Analysis, Elsevier, Elsevier, vol. 73(C), pages 103-111.
    2. Xueying Zheng & Wing Fung & Zhongyi Zhu, 2013. "Robust estimation in joint mean–covariance regression model for longitudinal data," Annals of the Institute of Statistical Mathematics, Springer, vol. 65(4), pages 617-638, August.


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