@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}
}
