@article {3364,
	title = {A Bayesian stochastic model for batting performance evaluation in one-day cricket},
	journal = {Journal of Quantitative Analysis in Sports},
	volume = {10},
	year = {2014},
	pages = {1{\textendash}13},
	keywords = {Gibbs sampling, Hidden Markov model, parallel sampling, reliability analysis},
	doi = {10.1515/jqas-2013-0057},
	url = {http://www.degruyter.com/view/j/jqas.2014.10.issue-1/jqas-2013-0057/jqas-2013-0057.xml},
	author = {Koulis, Theodoro and Muthukumarana, Saman and Briercliffe, Creagh}
}
@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}
}
@article {wu_nonlinear_2004,
	title = {Nonlinear mixed-effect models with nonignorably missing covariates},
	journal = {Canadian Journal of Statistics},
	volume = {32},
	number = {1},
	year = {2004},
	month = {mar},
	pages = {27{\textendash}37},
	abstract = {Nonlinear mixed-effect models are often used in the analysis of longitudinal data. However, it sometimes happens that missing values for some of the model covariates are not purely random. Motivated by an application to HTV viral dynamics, where this situation occurs, the author considers likelihood inference for this type of problem. His approach involves a Monte Carlo EM algorithm, along with a Gibbs sampler and rejection/importance sampling methods. A concrete application is provided.},
	keywords = {EM algorithm, Gibbs sampling, Longitudinal data, missing data, Rejection sampling},
	issn = {1708-945X},
	doi = {10.2307/3315997},
	url = {http://onlinelibrary.wiley.com/doi/10.2307/3315997/abstract},
	author = {WU, LANG}
}
