@article {wu_approximate_2008,
	title = {An approximate method for nonlinear mixed-effects models with nonignorably missing covariates},
	journal = {Statistics \& Probability Letters},
	volume = {78},
	number = {4},
	year = {2008},
	pages = {384{\textendash}389},
	abstract = {Nonlinear mixed-effect (NLME) models are very useful in many longitudinal studies. In practice, covariates in NLME models may contain missing data, and the missing data may be nonignorable. Likelihood inference for NLME models with missing covariates can be computationally very intensive. We propose a computationally much more efficient approximate method for NLME models with nonignorably missing covariates. We illustrate the method using a real data example.},
	keywords = {EM algorithm, Linearization, Longitudinal data, Taylor expansion},
	issn = {0167-7152},
	doi = {10.1016/j.spl.2007.07.011},
	url = {http://www.sciencedirect.com/science/article/pii/S0167715207002519},
	author = {WU, LANG}
}
@article {wu_computationally_2007,
	title = {A computationally efficient method for nonlinear mixed-effects models with nonignorable missing data in time-varying covariates},
	journal = {Computational Statistics \& Data Analysis},
	volume = {51},
	number = {5},
	year = {2007},
	pages = {2410{\textendash}2419},
	abstract = {Nonlinear mixed-effects (NLME) models are widely used for longitudinal data analyses. Time-dependent covariates are often introduced to partially explain inter-individual variation. These covariates often have missing data, and the missingness may be nonignorable. Likelihood inference for NLME models with nonignorable missing data in time-varying covariates can be computationally very intensive and may even offer computational difficulties such as nonconvergence. We propose a computationally very efficient method for approximate likelihood inference. The method is illustrated using a real data example.},
	keywords = {EM algorithm, Linearization, Longitudinal data, Random effects model},
	issn = {0167-9473},
	doi = {10.1016/j.csda.2006.07.036},
	url = {http://www.sciencedirect.com/science/article/pii/S0167947306002556},
	author = {WU, LANG}
}
@article {wu_generalized_2006,
	title = {Generalized linear mixed models with informative dropouts and missing covariates},
	journal = {Metrika},
	volume = {66},
	number = {1},
	year = {2006},
	month = {aug},
	pages = {1{\textendash}18},
	abstract = {Generalized linear mixed models (GLMM) are useful in many longitudinal data analyses. In the presence of informative dropouts and missing covariates, however, standard complete-data methods may not be applicable. In this article, we consider a likelihood method and an approximate method for GLMM with informative dropouts and missing covariates. The methods are implemented by Monte{\textendash}Carlo EM algorithms combined with Gibbs sampler. The approximate method may lead to inconsistent estimators but is computationally more efficient than the likelihood method. The two methods are evaluated via a simulation study for longitudinal binary data, and appear to perform reasonably well. A dataset on mental distress is analyzed in details.},
	keywords = {Economic Theory, general, Gibbs sampling, Linearization, Probability Theory and Stochastic Processes, PX-EM algorithm, Rejection sampling, Statistics, Statistics for Business/Economics/Mathematical Finance/Insurance},
	issn = {0026-1335, 1435-926X},
	doi = {10.1007/s00184-006-0083-6},
	url = {http://link.springer.com/article/10.1007/s00184-006-0083-6},
	author = {Wu, Kunling and WU, LANG}
}
