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dc.contributor.author Zhou, X
dc.contributor.author Reiter, JP
dc.date.accessioned 2011-04-15T16:46:16Z
dc.date.issued 2010-05-01
dc.identifier.citation American Statistician, 2010, 64 (2), pp. 159 - 163
dc.identifier.issn 0003-1305
dc.identifier.uri http://hdl.handle.net/10161/3380
dc.description.abstract This article is aimed at practitioners who plan to use Bayesian inference on multiply-imputed datasets in settings where posterior distributions of the parameters of interest are not approximately Gaussian. We seek to steer practitioners away from a naive approach to Bayesian inference, namely estimating the posterior distribution in each completed dataset and averaging functionals of these distributions. We demonstrate that this approach results in unreliable inferences. A better approach is to mix draws from the posterior distributions from each completed dataset, and use the mixed draws to summarize the posterior distribution. Using simulations, we show that for this second approach to work well, the number of imputed datasets should be large. In particular, five to ten imputed datasets "which is the standard recommendation for multiple imputation" is generally not enough to result in reliable Bayesian inferences. © 2010 American Statistical Association.
dc.format.extent 159 - 163
dc.language.iso en_US en_US
dc.relation.ispartof American Statistician
dc.relation.isversionof 10.1198/tast.2010.09109
dc.title A note on Bayesian inference after multiple imputation
dc.type Journal Article
dc.description.version Version of Record en_US
duke.date.pubdate 2010-5-0 en_US
duke.description.endpage 163 en_US
duke.description.issue 2 en_US
duke.description.startpage 159 en_US
duke.description.volume 64 en_US
dc.relation.journal American Statistician en_US
pubs.issue 2
pubs.organisational-group /Duke
pubs.organisational-group /Duke/Institutes and Provost's Academic Units
pubs.organisational-group /Duke/Institutes and Provost's Academic Units/Initiatives
pubs.organisational-group /Duke/Institutes and Provost's Academic Units/Initiatives/Energy Initiative
pubs.organisational-group /Duke/Sanford School of Public Policy
pubs.organisational-group /Duke/Sanford School of Public Policy/Center for Child and Family Policy
pubs.organisational-group /Duke/Sanford School of Public Policy/Duke Population Research Institute
pubs.organisational-group /Duke/Sanford School of Public Policy/Duke Population Research Institute/Duke Population Research Center
pubs.organisational-group /Duke/Trinity College of Arts & Sciences
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Statistical Science
pubs.publication-status Published
pubs.volume 64

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