Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1149741 | Journal of Statistical Planning and Inference | 2009 | 13 Pages |
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.
Related Topics
Physical Sciences and Engineering
Mathematics
Applied Mathematics
Authors
Guoyou Qin, Zhongyi Zhu, Wing K. Fung,