Shrinkage priors for Bayesian prediction

dc.creatorKomaki, Fumiyasu
dc.date2006-07-01
dc.date.accessioned2026-07-07T08:07:59Z
dc.date.available2026-07-07T08:07:59Z
dc.descriptionWe investigate shrinkage priors for constructing Bayesian predictive distributions. It is shown that there exist shrinkage predictive distributions asymptotically dominating Bayesian predictive distributions based on the Jeffreys prior or other vague priors if the model manifold satisfies some differential geometric conditions. Kullback--Leibler divergence from the true distribution to a predictive distribution is adopted as a loss function. Conformal transformations of model manifolds corresponding to vague priors are introduced. We show several examples where shrinkage predictive distributions dominate Bayesian predictive distributions based on vague priors.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053606000000010 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0607021
dc.identifierhttp://arxiv.org/abs/math/0607021
dc.identifierAnnals of Statistics 2006, Vol. 34, No. 2, 808-819
dc.identifierdoi:10.1214/009053606000000010
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131111
dc.subjectStatistics Theory
dc.subject62F15, 62C15 (Primary)
dc.titleShrinkage priors for Bayesian prediction
dc.typetext

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