@article { ISI:000232749700003,
	title = {Composite likelihood estimation in multivariate data analysis},
	journal = {Canadian Journal of Statistics {\textendash}- Revue Canadienne de Statistique},
	volume = {33},
	number = {3},
	year = {2005},
	note = {International Conference on Dependence Modelling - Statistical Theory and Applications in Finance and Insurance (DeMoSTAFI), Quebec City, CANADA, MAY 20-22, 2004},
	month = {SEP},
	pages = {335-356},
	abstract = {The authors propose two composite likelihood estimation procedures for multivariate models with regression/univariate and dependence parameters. One is a two-stage method based on both univariate and bivariate margins. The other estimates all the parameters simultaneously based on bivariate margins. For some special cases, the authors compare their asymptotic efficiencies with the maximum likelihood method. The performance of the two methods is reasonable, except that the first procedure is inefficient for the regression parameters under strong dependence. The second approach is generally better for the regression parameters, but less efficient for the dependence parameters under weak dependence.},
	issn = {0319-5724},
	doi = {10.1002/cjs.5540330303},
	author = {Zhao, Y and Joe, H}
}
