Mixtures of semiparametric varying coefficient models for longitudinal data with nonignorable dropout |
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Authors: | Zhi-qiang Li Liu-gen Xue |
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Institution: | [1]College of Sciences, Beijing University of Chemical Technology Beijing 100029, China [2]College of Applied Sciences, Beijing University of Technology Beijing 100022, China |
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Abstract: | Informative dropout often arise in longitudinal data. In this paper we propose a mixture model in which the responses follow
a semiparametric varying coefficient random effects model and some of the regression coefficients depend on the dropout time
in a non-parametric way. The local linear version of the profile-kernel method is used to estimate the parameters of the model.
The proposed estimators are shown to be consistent and asymptotically normal, and the finite performance of the estimators
is evaluated by numerical simulation. |
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Keywords: | Nonignorable dropout Estimating equation Profile-kernel Local linear estimation Longitudinal data Semiparametric varying coefficient |
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