Robust Estimation in Partial Linear Mixed Model for Longitudinal Data |
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Authors: | Qin Guoyou Zhu Zhongyi |
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Institution: | [1]Department of Statistics, East China Normal University, Shanghai 200062, China [2]Department of Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China [3]Department of Statistics, Fudan University, Shanghai 200333, China |
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Abstract: | In this article, robust generalized estimating equation for the analysis of par- tial linear mixed model for longitudinal data is used. The authors approximate the non- parametric function by a regression spline. Under some regular conditions, the asymptotic properties of the estimators are obtained. To avoid the computation of high-dimensional integral, a robust Monte Carlo Newton-Raphson algorithm is used. Some simulations are carried out to study the performance of the proposed robust estimators. In addition, the authors also study the robustness and the efficiency of the proposed estimators by simulation. Finally, two real longitudinal data sets are analyzed. |
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Keywords: | Generalized estimating equation longitudinal data metropolis algorithm mixed effect partial linear model robustness |
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