TY - JOUR
AU1 - Mardia, Kanti V.
AU2 - Kent, John T.
AU3 - Hughes, Gareth
AU4 - Taylor, Charles C.
AB - 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.
TI - Maximum likelihood estimation using composite likelihoods for closed exponential families
JF - Biometrika
DO - 10.1093/biomet/asp056
DA - 2009-12-29
UR - https://www.deepdyve.com/lp/oxford-university-press/maximum-likelihood-estimation-using-composite-likelihoods-for-closed-SPpF1E0BxK
SP - 975
EP - 982
VL - 96
IS - 4
DP - DeepDyve
ER -