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Robust estimation of generalized estimating equations with finite mixture correlation matrices and missing covariates at random for longitudinal data

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  • Tang, Niansheng
  • Wang, Wenjun
Abstract
This paper considers the parameter estimation problem in generalized estimating equations (GEEs) with a finite mixture of working correlation matrices and missing covariates at random for longitudinal data. A logistic regression model is adopted to specify missingness covariate mechanism, a weighted robust non-negative matrix projection (WNMFP) algorithm is developed to impute missing covariates, and a mixture GEE (mix-GEE) approach is proposed to estimate parameters. Under the KKT conditions, we show the convergence of the proposed WNMFP algorithm. Under some regularity conditions, we prove the consistence and asymptotic normality of the proposed mix-GEE estimator. Two simulation studies are conducted to assess the finite sample performance of the proposed methodologies. Empirical results show that the mix-GEE approach is more efficient and robust than the complete-case, inverse probability weighted and quadratic inference function methods. The data from an AIDS clinical trial is used to illustrate the proposed methodologies.

Suggested Citation

  • Tang, Niansheng & Wang, Wenjun, 2019. "Robust estimation of generalized estimating equations with finite mixture correlation matrices and missing covariates at random for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 640-655.
    • Handle: RePEc:eee:jmvana:v:173:y:2019:i:c:p:640-655
    DOI: 10.1016/j.jmva.2019.05.006
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    References listed on IDEAS

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    Cited by:

    1. Jing Sun, 2020. "An improvement on the efficiency of complete-case-analysis with nonignorable missing covariate data," Computational Statistics, Springer, vol. 35(4), pages 1621-1636, December.

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