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

Niansheng Tang and Wenjun Wang

Journal of Multivariate Analysis, 2019, vol. 173, issue C, 640-655

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.

Keywords: Finite mixture correlation matrices; Inverse probability weighting; Missing at random; Weighted robust non-negative matrix projection; Generalized estimating equations (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.jmva.2019.05.006

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