A sequential reduction method for inference in generalized linear mixed models
A sequential reduction method for inference in generalized linear mixed models
The likelihood for the parameters of a generalized linear mixed model involves an integral which may be of very high dimension. Because of this intractability, many approximations to the likelihood have been proposed, but all can fail when the model is sparse, in that there is only a small amount of information available on each random effect. The sequential reduction method described in this paper exploits the dependence structure of the posterior distribution of the random effects to reduce substantially the cost of finding an accurate approximation to the likelihood in models with sparse structure.
Graphical model, Intractable likelihood, Laplace approximation, Pairwise comparison, Sparse grid interpolation
135-152
Ogden, Helen E.
78b03322-3836-4d3b-8b84-faf12895854e
2015
Ogden, Helen E.
78b03322-3836-4d3b-8b84-faf12895854e
Ogden, Helen E.
(2015)
A sequential reduction method for inference in generalized linear mixed models.
Electronic Journal of Statistics, 9 (1), .
(doi:10.1214/15-EJS991).
Abstract
The likelihood for the parameters of a generalized linear mixed model involves an integral which may be of very high dimension. Because of this intractability, many approximations to the likelihood have been proposed, but all can fail when the model is sparse, in that there is only a small amount of information available on each random effect. The sequential reduction method described in this paper exploits the dependence structure of the posterior distribution of the random effects to reduce substantially the cost of finding an accurate approximation to the likelihood in models with sparse structure.
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e-pub ahead of print date: 6 February 2015
Published date: 2015
Keywords:
Graphical model, Intractable likelihood, Laplace approximation, Pairwise comparison, Sparse grid interpolation
Identifiers
Local EPrints ID: 413750
URI: http://eprints.soton.ac.uk/id/eprint/413750
ISSN: 1935-7524
PURE UUID: 3904962f-375b-447e-8901-bfd02a50466a
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Date deposited: 04 Sep 2017 16:30
Last modified: 16 Mar 2024 04:17
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