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Robust estimating equations and bias correction of correlation parameters for longitudinal data

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  • Qin, Guo You
  • Zhu, Zhong Yi
  • Fung, Wing K.

Abstract

The estimation of correlation parameters has received attention for both its own interest and improvement of the estimation efficiency of mean parameters by the generalized estimating equations (GEE) approach. Many of the well-established methods for the estimation of correlation parameters can be constructed under the GEE framework which is, however, sensitive to outliers. In this paper, we consider two ways of constructing robust estimating equations for achieving robust estimation of the correlation parameters. Furthermore, the estimators of the correlation parameters from the robustified GEE may be still biased as the expectation of the estimating equation is biased from zero when the underlying distribution is not symmetric. Therefore, bias-corrected robust estimators of correlation parameters are proposed. The performance of the proposed methods are investigated by simulation. The results show that the proposed robust and bias-corrected robust estimators can reduce the bias successfully. Two real data sets are analyzed for illustration.

Suggested Citation

  • Qin, Guo You & Zhu, Zhong Yi & Fung, Wing K., 2008. "Robust estimating equations and bias correction of correlation parameters for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4745-4753, June.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:10:p:4745-4753
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    References listed on IDEAS

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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, vol. 100, pages 1176-1184, December.
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    5. You-Gan Wang & Xu Lin & Min Zhu, 2005. "Robust Estimating Functions and Bias Correction for Longitudinal Data Analysis," Biometrics, The International Biometric Society, vol. 61(3), pages 684-691, September.
    6. Cantoni E. & Ronchetti E., 2001. "Robust Inference for Generalized Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1022-1030, September.
    7. Kelvin K. W. Yau & Anthony Y. C. Kuk, 2002. "Robust estimation in generalized linear mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(1), pages 101-117, January.
    8. Xuming He, 2002. "Estimation in a semiparametric model for longitudinal data with unspecified dependence structure," Biometrika, Biometrika Trust, vol. 89(3), pages 579-590, August.
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    Cited by:

    1. Hines, R.J. O'Hara & Hines, W.G.S., 2010. "Indices for covariance mis-specification in longitudinal data analysis with no missing responses and with MAR drop-outs," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 806-815, April.

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