Composite likelihood estimation in multivariate data analysis

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Composite likelihood estimation in multivariate data analysis

TitleComposite likelihood estimation in multivariate data analysis
Publication TypeJournal Article
Year of Publication2005
AuthorsZhao, Y, Joe, H
JournalCanadian Journal of Statistics –- Revue Canadienne de Statistique
Volume33
Pagination335-356
Date PublishedSEP
ISSN0319-5724
AbstractThe 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.
DOI10.1002/cjs.5540330303