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Pseudo-likelihood inference underestimates model uncertainty: evidence from bayesian nearest neighbours

dc.contributor.authorSu, Wanhua
dc.contributor.authorChipman, Hugh A.
dc.contributor.authorZhu, Mu
dc.date.accessioned2020-12-01
dc.date.accessioned2022-05-31T01:16:38Z
dc.date.available2022-05-31T01:16:38Z
dc.date.issued2011
dc.description.abstractWhen using the K-nearest neighbours (KNN) method, one often ignores the uncertainty in the choice of K. To account for such uncertainty, Bayesian KNN (BKNN) has been proposed and studied (Holmes and Adams 2002 Cucala et al. 2009). We present some evidence to show that the pseudo-likelihood approach for BKNN, even after being corrected by Cucala et al. (2009), still significantly underestimates model uncertainty.
dc.format.extent166.01KB
dc.format.mimetypePDF
dc.identifier.citationSu, W., Chipman, H., & Zhu, M. (2011). Pseudo-likelihood inference underestimates model uncertainty: evidence from bayesian nearest neighbours. Journal of the Iranian Statistical Society, 2(2), 167-180. http://jirss.irstat.ir/article-1-162-en.html
dc.identifier.urihttps://hdl.handle.net/20.500.14078/2080
dc.languageEnglish
dc.language.isoen
dc.rightsAll Rights Reserved
dc.subjectbootstrap interval
dc.subjectMCMC
dc.subjectposterior interval
dc.subjectpseudolikelihood
dc.titlePseudo-likelihood inference underestimates model uncertainty: evidence from bayesian nearest neighboursen
dc.typeArticle
dspace.entity.type

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