Robust estimation of covariance parameters in partial linear model for longitudinal data |
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Authors: | Guoyou Qin Zhongyi Zhu Wing K Fung |
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Institution: | 1. Department of Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China;2. Department of Statistics, Fudan University, Shanghai 200433, China;3. Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China |
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Abstract: | For longitudinal data, the within-subject dependence structure and covariance parameters may be of practical and theoretical interests. The estimation of covariance parameters has received much attention and been studied mainly in the framework of generalized estimating equations (GEEs). The GEEs method, however, is sensitive to outliers. In this paper, an alternative set of robust generalized estimating equations for both the mean and covariance parameters are proposed in the partial linear model for longitudinal data. The asymptotic properties of the proposed estimators of regression parameters, non-parametric function and covariance parameters are obtained. Simulation studies are conducted to evaluate the performance of the proposed estimators under different contaminations. The proposed method is illustrated with a real data analysis. |
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Keywords: | B-spline Covariance parameters Generalized estimating equations Longitudinal data Partial linear models Robustness |
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