Confronting Prior Convictions: On Issues of Prior Sensitivity and Likelihood Robustness in Bayesian Analysis

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dc.contributor.authorHEDIBERT FREITAS LOPES
dc.contributor.authorTobias, Justin L.
dc.coverage.cidadeNão informadopt_BR
dc.coverage.paisNão Informadopt_BR
dc.creatorTobias, Justin L.
dc.date.accessioned2022-08-18T18:39:28Z
dc.date.available2022-08-18T18:39:28Z
dc.date.issued2011
dc.description.otherIn this review we explore issues of the sensitivity of Bayes estimates to the prior and form of the likelihood. With respect to the prior, we argue that non-Bayesian analyses also incorporate prior information, illustrate that the Bayes posterior mean and the frequentist maximum likelihood estimator are often asymptotically equivalent, review a simple computational strategy for analyzing sensitivity to the prior in practice, and finally document the potentially important role of the prior in Bayesian model comparison. With respect to issues of likelihood robustness, we review a variety of computational strategies for significantly expanding the maintained sampling model, including the use of finite Gaussian mixture models and models based on Dirichlet process priors.pt_BR
dc.format.extentp. 107-131pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.doi10.1146/annurev-economics-111809-125134pt_BR
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/4043
dc.language.isoInglêspt_BR
dc.publisherNão informadopt_BR
dc.relation.ispartofAnnual Review of Economicspt_BR
dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DOS USUÁRIOS INDIVIDUAIS VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITOR.pt_BR
dc.subject.keywordsBayesian methodspt_BR
dc.subject.keywordsmarginal likelihoodpt_BR
dc.subject.keywordsscale mixture of normalspt_BR
dc.subject.keywordsDirichlet process mixturept_BR
dc.subject.keywordsfactor modelspt_BR
dc.subject.keywordsMarkov chain Monte Carlopt_BR
dc.subject.keywordsGibbs samplerpt_BR
dc.subject.keywordssequential Monte Carlopt_BR
dc.titleConfronting Prior Convictions: On Issues of Prior Sensitivity and Likelihood Robustness in Bayesian Analysispt_BR
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://www.annualreviews.org/doi/pdf/10.1146/annurev-economics-111809-125134
local.subject.cnpqCiências Sociais Aplicadaspt_BR
local.typeArtigo Científicopt_BR
relation.isAuthorOfPublication41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
relation.isAuthorOfPublication.latestForDiscovery41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
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