Open Access
2015 A sequential reduction method for inference in generalized linear mixed models
Helen E. Ogden
Electron. J. Statist. 9(1): 135-152 (2015). DOI: 10.1214/15-EJS991


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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Helen E. Ogden. "A sequential reduction method for inference in generalized linear mixed models." Electron. J. Statist. 9 (1) 135 - 152, 2015.


Received: 1 August 2014; Published: 2015
First available in Project Euclid: 6 February 2015

zbMATH: 1307.62057
MathSciNet: MR3306573
Digital Object Identifier: 10.1214/15-EJS991

Primary: 62F10
Secondary: 62J15

Keywords: Graphical model , intractable likelihood , Laplace approximation , pairwise comparison , sparse grid interpolation

Rights: Copyright © 2015 The Institute of Mathematical Statistics and the Bernoulli Society

Vol.9 • No. 1 • 2015
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