Decoupling Shrinkage and Selection in Gaussian Linear Factor Analysis

dc.contributor.authorBolfarine, Henrique
dc.contributor.authorCarvalho, Carlos M.
dc.contributor.authorHEDIBERT FREITAS LOPES
dc.contributor.authorMurray, Jared S.
dc.creatorBolfarine, Henrique
dc.creatorCarvalho, Carlos M.
dc.creatorMurray, Jared S.
dc.date.accessioned2024-10-28T18:55:10Z
dc.date.available2024-10-28T18:55:10Z
dc.date.issued2024
dc.description.abstractFactor analysis is a popular method for modeling dependence in multivariate data. However, determining the number of factors and obtaining a sparse orientation of the loadings are still major challenges. In this paper, we propose a decision-theoretic approach that brings to light the relationship between model fit, factor dimension, and sparse loadings. This relation is done through a summary of the information contained in the multivariate posterior. A two-step strategy is used in our method. First, given the posterior samples from the Bayesian factor analysis model, a series of point estimates with a decreasing number of factors and different levels of sparsity are recovered by minimizing an expected penalized loss function. Second, the degradation in model fit between the posterior of the full model and the recovered estimates is displayed in a summary. In this step, a criterion is proposed for selecting the factor model with the best trade-off between fit, sparseness, and factor dimension. The findings are illustrated through a simulation study and an application to personality data. We used different prior choices to show the flexibility of the proposed method.en
dc.formatFísico
dc.format.extentp. 181 – 203
dc.identifier.doi10.1214/22-BA1349
dc.identifier.urihttps://repositorio.insper.edu.br/handle/11224/7184
dc.language.isoInglês
dc.relation.isboundProdução vinculada ao Núcleo de Ciências de Dados e Decisão
dc.relation.ispartofBayesian Anal
dc.subjectBayesian factor analysisen
dc.subjectModel selectionen
dc.subjectSparse loadingsen
dc.subjectFactor dimensionen
dc.subjectLoss functionen
dc.titleDecoupling Shrinkage and Selection in Gaussian Linear Factor Analysis
dc.typejournal article
dspace.entity.typePublication
local.identifier.sourceUrihttps://projecteuclid.org/journals/bayesian-analysis/volume-19/issue-1/Decoupling-Shrinkage-and-Selection-in-Gaussian-Linear-Factor-Analysis/10.1214/22-BA1349.full
local.publisher.countryNão Informado
local.subject.cnpqCIENCIAS EXATAS E DA TERRA::MATEMATICA
local.subject.cnpqCIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA
local.subject.cnpqENGENHARIAS::ENGENHARIA ELETRICA
local.subject.cnpqCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
local.typeArtigo Científico
publicationissue.issueNumber1
publicationvolume.volumeNumber19
relation.isAuthorOfPublication41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca
relation.isAuthorOfPublication.latestForDiscovery41f844cb-0e5a-4ef1-bb19-5ab1cec8e2ca

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