期刊
BIOMETRIKA
卷 96, 期 4, 页码 975-982出版社
OXFORD UNIV PRESS
DOI: 10.1093/biomet/asp056
关键词
Bivariate von Mises distribution; Closed exponential family; Fisher information; Log-linear model; Maximum likelihood; Multivariate normal distribution; Pseudolikelihood
资金
- U.K. Engineering and Physical Sciences Research Council
In certain multivariate problems the full probability density has an awkward normalizing constant, but the conditional and/or marginal distributions may be much more tractable. In this paper we investigate the use of composite likelihoods instead of the full likelihood. For closed exponential families, both are shown to be maximized by the same parameter values for any number of observations. Examples include log-linear models and multivariate normal models. In other cases the parameter estimate obtained by maximizing a composite likelihood can be viewed as an approximation to the full maximum likelihood estimate. An application is given to an example in directional data based on a bivariate von Mises distribution.
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