Gaussian variational approximate inference for generalized linear mixed models

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dc.contributor.author Ormerod J. en_US
dc.contributor.author Wand Matthew en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-10-12T03:32:47Z
dc.date.available 2012-10-12T03:32:47Z
dc.date.issued 2011 en_US
dc.identifier 2010005185 en_US
dc.identifier.citation Ormerod J. and Wand Matthew 2011, 'Gaussian variational approximate inference for generalized linear mixed models', American Statistical Association, vol. 21, no. 1, pp. 2-17. en_US
dc.identifier.issn 1061-8600 en_US
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/17930
dc.description.abstract Variational approximation methods have become a mainstay of contemporary machine learning methodology, but currently have little presence in statistics. We devise an effective variational approximation strategy for fitting generalized linear mixed models (GLMMs) appropriate for grouped data. It involves Gaussian approximation to the distributions of random effects vectors, conditional on the responses. We show that Gaussian variational approximation is a relatively simple and natural alternative to Laplace approximation for fast, non-Monte Carlo, GLMM analysis. Numerical studies show Gaussian variational approximation to be very accurate in grouped data GLMM contexts. Finally, we point to some recent theory on consistency of Gaussian variational approximation in this context. en_US
dc.language en_US
dc.publisher American Statistical Association en_US
dc.relation.isbasedon http://dx.doi.org/10.1198/jcgs.2011.09118 en_US
dc.title Gaussian variational approximate inference for generalized linear mixed models en_US
dc.parent Journal of Computational and Graphical Statistics en_US
dc.journal.volume 21 en_US
dc.journal.number 1 en_US
dc.publocation United States en_US
dc.identifier.startpage 2 en_US
dc.identifier.endpage 17 en_US
dc.cauo.name SCI.Mathematical Sciences en_US
dc.conference Verified OK en_US
dc.for 010400 en_US
dc.personcode 0000070442;110509 en_US
dc.percentage 000100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Best prediction; Likelihood-based inference; Longitudinal data analysis; Machine learning; Variance components en_US
dc.staffid en_US


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