@article { ISI:000342542300004,
	title = {Model comparison with composite likelihood information criteria},
	journal = {Bernoulli},
	volume = {20},
	number = {4},
	year = {2014},
	month = {NOV},
	pages = {1738-1764},
	publisher = {Int Statistical Inst},
	type = {Article},
	abstract = {Comparisons are made for the amount of agreement of the composite likelihood information criteria and their full likelihood counterparts when making decisions among the fits of different models; and some properties of penalty term for composite likelihood information criteria are obtained. Asymptotic theory is given for the case when a simpler model is nested within a bigger model, and the bigger model approaches the simpler model under a sequence of local alternatives. Composite likelihood can more or less frequently choose the bigger model, depending on the direction of local alternatives; in the former case, composite likelihood has more {\textquoteleft}{\textquoteleft}power{\textquoteright}{\textquoteright} to choose the bigger model. The behaviors of the information criteria are illustrated via theory and simulation examples of the Gaussian linear mixed-effects model.},
	keywords = {Akaike information criterion, Bayesian information criterion, local alternatives, mixed-effects model, Model comparison},
	issn = {1350-7265},
	doi = {10.3150/13-BEJ539},
	author = {Ng, C. T. and Joe, Harry}
}
@article {wu_simultaneous_2004,
	title = {Simultaneous inference for longitudinal data with detection limits and covariates measured with errors, with application to AIDS studies},
	journal = {Statistics in Medicine},
	volume = {23},
	number = {11},
	year = {2004},
	month = {jun},
	pages = {1715{\textendash}1731},
	abstract = {In AIDS studies such as HIV viral dynamics, statistical inference is often complicated because the viral load measurements may be subject to left censoring due to a detection limit and time-varying covariates such as CD4 counts may be measured with substantial errors. Mixed-effects models are often used to model the response and the covariate processes in these studies. We propose a unified approach which addresses the censoring and measurement errors simultaneously. We estimate the model parameters by a Monte-Carlo EM algorithm via the Gibbs sampler. A simulation study is conducted to compare the proposed method with the usual two-step method and a naive method. We find that the proposed method produces approximately unbiased estimates with more reliable standard errors. A real data set from an AIDS study is analysed using the proposed method. Copyright {\textcopyright} 2004 John Wiley \& Sons, Ltd.},
	keywords = {EM algorithm, Gibbs sampler, HIV, mixed-effects model},
	issn = {1097-0258},
	doi = {10.1002/sim.1748},
	url = {http://onlinelibrary.wiley.com/doi/10.1002/sim.1748/abstract},
	author = {WU, LANG}
}
