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dc.contributor.authorBosq, Denis
dc.contributor.authorRuiz-Medina, María Dolores
dc.date.accessioned2014-10-20T11:58:30Z
dc.date.available2014-10-20T11:58:30Z
dc.date.issued2014
dc.identifier.citationBosq, D.; Ruiz-Medina, M.D. Bayesian estimation in a high dimensional parameter framework. Electronic Journal of Statistics, 8(1): 1604-1640 (2014). [http://hdl.handle.net/10481/33445]es_ES
dc.identifier.issn1935-7524
dc.identifier.urihttp://hdl.handle.net/10481/33445
dc.description.abstractSufficient conditions are derived for the asymptotic efficiency and equivalence of componentwise Bayesian and classical estimators of the infinite-dimensional parameters characterizing l2 valued Poisson process, and Hilbert valued Gaussian random variable models. Conjugate families are considered for the Poisson and Gaussian univariate likelihoods, in the Bayesian estimation of the components of such infinite-dimensional parameters. In the estimation of the functional mean of a Hilbert valued Gaussian random variable, sufficient and necessary conditions, that ensure a better performance of the Bayes estimator with respect to the classical one, are also obtained for the finite-sample size case. A simulation study is carried out to provide additional information on the relative efficiency of Bayes and classical estimators in a high-dimensional framework.es_ES
dc.description.sponsorshipThis work has been supported in part by projects MTM2012-32674 of the DGI (co-funded with FEDER funds), MEC, Spain.es_ES
dc.language.isoenges_ES
dc.publisherInstitute of Mathematical Statisticses_ES
dc.subjectAsymptotic relative efficiencyes_ES
dc.subjectBayesian estimationes_ES
dc.subjectHilbert valued Gaussian random variablees_ES
dc.subjectHilbert valued Poisson processes_ES
dc.titleBayesian estimation in a high dimensional parameter frameworkes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1214/14-EJS935


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