2009.47: Semiparametric Mean-Covariance Regression Analysis for Longitudinal Data
2009.47: Chenlei Leng, Weiping Zhang and Jianxin Pan (2009) Semiparametric Mean-Covariance Regression Analysis for Longitudinal Data.
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E±cient estimation of the regression coe±cients in longitudinal data anal- ysis requires a correct speci¯cation of the covariance structure. Existing ap- proaches usually focus on modeling the mean with speci¯cation of certain co- variance structures, which may lead to ine±cient or biased estimators of pa- rameters in the mean if misspeci¯cation occurs. In this paper, we propose a data-driven approach based on semiparametric regression models for the mean and the covariance simultaneously, motivated by the modi¯ed Cholesky de- composition. A regression spline based approach using generalized estimating equations is developed to estimate the parameters in the mean and the covari- ance. The resulting estimators for the regression coe±cients in both the mean and the covariance are shown to be consistent and asymptotically normally dis- tributed. In addition, the nonparametric functions in these two structures are estimated at their optimal rate of convergence. Simulation studies and a real data analysis show that the proposed approach yields highly e±cient estimators for the parameters in the mean, and provides parsimonious estimation for the covariance structure.
|Item Type:||MIMS Preprint|
|Uncontrolled Keywords:||Covariance misspecification; Efficiency; Generalized estimating equation; Longitudinal data; Modified Cholesky decomposition; Semiparametric models|
|Subjects:||MSC 2000 > 62 Statistics|
|Deposited By:||Ms Lucy van Russelt|
|Deposited On:||09 July 2009|