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A sequential reduction method for inference in generalized linear mixed models
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Ogden, Helen E. (2015) A sequential reduction method for inference in generalized linear mixed models. Electronic Journal of Statistics, Volume 9 . pp. 135-152. doi:10.1214/15-EJS991 ISSN 1935-7524.
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Official URL: http://dx.doi.org/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.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Library of Congress Subject Headings (LCSH): | Linear models (Statistics), Graphical modeling (Statistics), Sparse matrices | ||||
Journal or Publication Title: | Electronic Journal of Statistics | ||||
Publisher: | Institute of Mathematical Statistics | ||||
ISSN: | 1935-7524 | ||||
Official Date: | 6 February 2015 | ||||
Dates: |
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Volume: | Volume 9 | ||||
Page Range: | pp. 135-152 | ||||
DOI: | 10.1214/15-EJS991 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Open Access (Creative Commons) | ||||
Date of first compliant deposit: | 29 December 2015 | ||||
Date of first compliant Open Access: | 29 December 2015 | ||||
Funder: | Engineering and Physical Sciences Research Council (EPSRC) | ||||
Grant number: | EP/P50578X/1 (EPSRC), EP/K014463/1 (EPSRC) |
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