NDLI: Simultaneous Inference for Semiparametric Nonlinear Mixed-Effects Models with Covariate Measurement Errors and Missing Responses

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Simultaneous Inference for Semiparametric Nonlinear Mixed-Effects Models with Covariate Measurement Errors and Missing Responses

Content Provider	Scilit
Author	Liu, Wei Wu, Lang
Copyright Year	2006
Description	Journal: Biometrics Semiparametric nonlinear mixed-effects (NLME) models are flexible for modeling complex longitudinal data. Covariates are usually introduced in the models to partially explain interindividual variations. Some covariates, however, may be measured with substantial errors. Moreover, the responses may be missing and the missingness may be nonignorable. We propose two approximate likelihood methods for semiparametric NLME models with covariate measurement errors and nonignorable missing responses. The methods are illustrated in a real data example. Simulation results show that both methods perform well and are much better than the commonly used naive method.
Related Links	http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2006.00687.x/pdf
Ending Page	350
Page Count	9
Starting Page	342
e-ISSN	15410420
DOI	10.1111/j.1541-0420.2006.00687.x
Journal	Biometrics
Issue Number	2
Volume Number	63
Language	English
Publisher	Wiley-Blackwell
Publisher Date	2006-12-07
Access Restriction	Open
Subject Keyword	Journal: Biometrics Statistics and Probability Cubic Spline Basis Longitudinal Data Monte Carlo Em Algorithm Random‐effects Model
Content Type	Text
Resource Type	Article

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