Parsimonious Bayesian Factor Analysis when the Number of Factors is Unknown

dc.contributor.authorFruhwirth-Schnatter, Sylvia
dc.contributor.authorHEDIBERT FREITAS LOPES
dc.coverage.cidadeSão Paulopt_BR
dc.coverage.paisBrasilpt_BR
dc.creatorFruhwirth-Schnatter, Sylvia
dc.date.accessioned2023-07-25T13:02:07Z
dc.date.available2023-07-25T13:02:07Z
dc.date.issued2014
dc.description.otherWe introduce a new and general set of identifiability conditions for factor models which handles the ordering problem associated with current common practice. In addition, the new class of parsimonious Bayesian factor analysis leads to a factor loading matrix repre sentation which is an intuitive and easy to implement factor selection scheme. We argue that the structuring the factor loadings matrix is in concordance with recent trends in applied factor analysis. Our MCMC scheme for posterior inference makes several improvements over the existing alternatives while outlining various strategies for conditional posterior in ference in a factor selection scenario. Four applications, two based on synthetic data and two based on well known real data, are introduced to illustrate the applicability and gener ality of the new class of parsimonious factor models, as well as to highlight features of the proposed sampling schemes.pt_BR
dc.format.extent38 p.pt_BR
dc.format.mediumDigitalpt_BR
dc.identifier.issueWPE 345/2014
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/5942
dc.language.isoInglêspt_BR
dc.publisherInsperpt_BR
dc.relation.ispartofseriesInsper Working Paperpt_BR
dc.rights.licenseO INSPER E ESTE REPOSITÓRIO NÃO DETÊM OS DIREITOS DE USO E REPRODUÇÃO DOS CONTEÚDOS AQUI REGISTRADOS. É RESPONSABILIDADE DO USUÁRIO VERIFICAR OS USOS PERMITIDOS NA FONTE ORIGINAL, RESPEITANDO-SE OS DIREITOS DE AUTOR OU EDITORpt_BR
dc.subject.keywordsIdentifiabilitypt_BR
dc.subject.keywordsparsimonypt_BR
dc.subject.keywordsCholesky decompositionpt_BR
dc.subject.keywordsrank defficiencypt_BR
dc.subject.keywordsHeywood problempt_BR
dc.titleParsimonious Bayesian Factor Analysis when the Number of Factors is Unknownpt_BR
dc.typeworking paper
dspace.entity.typePublication
local.subject.cnpqCiências Exatas e da Terrapt_BR
local.typeWorking Paperpt_BR
relation.isAuthorOfPublication41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
relation.isAuthorOfPublication.latestForDiscovery41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
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