@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}
}
