@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 {perlman_improved_2006,
	title = {Some Improved Tests for Multivariate One-Sided Hypotheses},
	journal = {Metrika},
	volume = {64},
	number = {1},
	year = {2006},
	month = {feb},
	pages = {23{\textendash}39},
	abstract = {Multivariate one-sided hypothesis-testing problems are very common in clinical trials with multiple endpoints. The likelihood ratio test (LRT) and union-intersection test (UIT) are widely used for testing such problems. It is argued that, for many important multivariate one-sided testing problems, the LRT and UIT fail to adapt to the presence of subregions of varying dimensionalities on the boundary of the null parameter space and thus give undesirable results. Several improved tests are proposed that do adapt to the varying dimensionalities and hence reflect the evidence provided by the data more accurately than the LRT and UIT. Moreover, the proposed tests are often less biased and more powerful than the LRT and UIT.},
	keywords = {Economic Theory, general, likelihood ratio test, Multiple endpoints, One-sided hypothesis, p-value, Probability Theory and Stochastic Processes, Statistics, Statistics for Business/Economics/Mathematical Finance/Insurance, Union-intersection test},
	issn = {0026-1335, 1435-926X},
	doi = {10.1007/s00184-006-0028-0},
	url = {http://link.springer.com/article/10.1007/s00184-006-0028-0},
	author = {Perlman, Michael D. and WU, LANG}
}
