Bayesian analysis of reduced rank regression models using post-processing

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dc.contributor.authorAßmann, Christian
dc.contributor.authorBoysen-Hogrefe, Jens
dc.contributor.authorPape, Markus
dc.date.accessioned2021-06-28T12:41:33Z
dc.date.available2021-06-28T12:41:33Z
dc.date.issued2021
dc.description.abstractBayesian estimation of reduced rank regression models requires careful consideration of the well known identification problem. We demonstrate that this identification problem can be handled efficiently by using prior distributions that restrict a part of the parameter space to the Stiefel manifold and post-processing the obtained Gibbs sampler output according to an appropriately specified loss function. This extends the possibilities for Bayesian inference in reduced rank regression models. Besides inference, we also discuss model selection in terms of posterior predictive assessment. We choose this approach because computing the marginal data likelihood under the identifying restrictions implies prohibitive computational burden. We illustrate the proposed approach with a simulation study and an empirical application.en
dc.identifier.urihttp://hdl.handle.net/2003/40285
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22158
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;13/2021en
dc.subjectBayesian estimationen
dc.subjectposterior predictive assessmenten
dc.subjectStiefel manifolden
dc.subjectmodel selectionen
dc.subjectorthogonal transformationen
dc.subjectreduced rank regressionen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleBayesian analysis of reduced rank regression models using post-processingen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access
eldorado.secondarypublicationfalsede

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