@article { ISI:000366777800013,
	title = {Comparison of non-nested models under a general measure of distance},
	journal = {Journal of Statistical Planning and Inference},
	volume = {170},
	year = {2016},
	month = {MAR},
	pages = {166-185},
	publisher = {Elsevier Science BV},
	type = {Article},
	abstract = {As a supplement to summary statistics of information criteria, the closeness of two or more competing non-nested models can be compared under a procedure that is more general than that proposed in Vuong (1989); measures of closeness other than the Kullback-Leibler divergence are allowed. Large deviation theory is used to obtain a bound of the power of rejecting the null hypothesis that the two models are equally close to the true model. Such a bound can be expressed in terms of a constant gamma is an element of [0, 1); gamma can be computed empirically without any knowledge of the data generating mechanism. Additionally, based on the constant gamma, the procedures constructed based on different measures of distance can be compared on their abilities to conclude a difference between two models. (C) 2015 Elsevier B.V. All rights reserved.},
	keywords = {Composite likelihood, Copula, Large deviation theory, Model comparison, Model misspecification},
	issn = {0378-3758},
	doi = {10.1016/j.jspi.2015.10.004},
	author = {Ng, C. T. and Joe, Harry}
}
@article { ISI:000294806800005,
	title = {Weighted scores method for regression models with dependent data},
	journal = {Biostatistics},
	volume = {12},
	number = {4},
	year = {2011},
	month = {OCT},
	pages = {653-665},
	publisher = {Oxford Univ Press},
	type = {Article},
	abstract = {<p>There are copula-based statistical models in the literature for regression with dependent data such as clustered and longitudinal overdispersed counts, for which parameter estimation and inference are straightforward. For situations where the main interest is in the regression and other univariate parameters and not the dependence, we propose a {\textquoteleft}{\textquoteleft}weighted scores method{\textquoteright}{\textquoteright}, which is based on weighting score functions of the univariate margins. The weight matrices are obtained initially fitting a discretized multivariate normal distribution, which admits a wide range of dependence. The general methodology is applied to negative binomial regression models. Asymptotic and small-sample efficiency calculations show that our method is robust and nearly as efficient as maximum likelihood for fully specified copula models. An illustrative example is given to show the use of our weighted scores method to analyze utilization of health care based on family characteristics.</p>
},
	keywords = {Composite likelihood, Copulas, Count data, Estimating equations, Negative binomial},
	issn = {1465-4644},
	doi = {10.1093/biostatistics/kxr005},
	author = {Nikoloulopoulos, Aristidis K. and Joe, Harry and Chaganty, N. R.}
}
@article { ISI:000263136700009,
	title = {On weighting of bivariate margins in pairwise likelihood},
	journal = {Journal of Multivariate Analysis},
	volume = {100},
	number = {4},
	year = {2009},
	month = {APR},
	pages = {670-685},
	publisher = {Elsevier Inc},
	type = {article},
	abstract = {Composite and pairwise likelihood methods have recently been increasingly used. For clustered data with varying cluster sizes, we study asymptotic relative efficiencies for various weighted pairwise likelihoods, with weight being a function of cluster size. For longitudinal data, we also study weighted pairwise likelihoods with weights that can depend on lag. Good choice of weights are needed to avoid the undesirable behavior of estimators with low efficiency. Some analytic results are obtained using the multivariate normal distribution. For clustered data, a practically good choice of weight is obtained after study of relative efficiencies for an exchangeable multivariate normal model; they are different from weights that had previously been suggested. For longitudinal data, there are advantages to only include bivariate margins of adjacent or nearly adjacent pairs in the weighted pairwise likelihood. (C) 2008 Elsevier Inc. All rights reserved.},
	keywords = {Binary probit, Clustered data, Composite likelihood, Longitudinal data},
	issn = {0047-259X},
	doi = {10.1016/j.jmva.2008.07.004},
	author = {Joe, Harry and Lee, Youngjo}
}
@article { ISI:000245010700005,
	title = {Limited information goodness-of-fit testing in multidimensional contingency tables},
	journal = {Psychometrika},
	volume = {71},
	number = {4},
	year = {2006},
	month = {DEC},
	pages = {713-732},
	publisher = {Springer},
	type = {Article},
	abstract = {We introduce a family of goodness-of-fit statistics for testing composite null hypotheses in multidimensional contingency tables. These statistics are quadratic forms in marginal residuals up to order r. They are asymptotically chi-square under the null hypothesis when parameters are estimated using any asymptotically normal consistent estimator. For a widely used item response model, when r is small and multidimensional tables are sparse, the proposed statistics have accurate empirical Type I errors, unlike Pearson{\textquoteright}s X-2. For this model in nonsparse situations, the proposed statistics are also more powerful than X-2. In addition, the proposed statistics are asymptotically chi-square when applied to subtables, and can be used for a piecewise goodness-of-fit assessment to determine the source of misfit in poorly fitting models.},
	keywords = {categorical data analysis, Composite likelihood, item response theory, Lisrel, multivariate discrete data, multivariate multinomial distribution},
	issn = {0033-3123},
	doi = {10.1007/s11336-005-1295-9},
	author = {Maydeu-Olivares, Albert and Joe, Harry}
}
