Dependent Bayesian nonparametric modeling of compositional data using random Bernstein polynomials

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dc.catalogadoraba
dc.contributor.authorWehrhahn, Claudia
dc.contributor.authorBarrientos, Andrés F.
dc.contributor.authorJara Vallejos, Alejandro Antonio
dc.date.accessioned2024-06-25T21:44:20Z
dc.date.available2024-06-25T21:44:20Z
dc.date.issued2022
dc.description.abstractWe discuss Bayesian nonparametric procedures for the regression analysis of compositional responses, that is, data supported on a multivariate simplex. The procedures are based on a modified class of multivariate Bernstein polynomials and on the use of dependent stick-breaking processes. A general model and two simplified versions of the general model are discussed. Appealing theoretical properties such as continuity, association structure, support, and consistency of the posterior distribution are established. Additionally, we exploit the use of spike-and-slab priors for choosing the version of the model that best adapts to the complexity of the underlying true data-generating distribution. The performance of the proposed model is illustrated in a simulation study and in an application to solid waste data from Colombia.
dc.description.funderANID
dc.description.funderMillennium Nucleus Center
dc.description.funderMillennium Science Initiative Program
dc.description.funderNSF-DMS
dc.description.funderCONICYT
dc.description.funderFONDECYT
dc.fechaingreso.objetodigital2024-06-25
dc.format.extent2405
dc.fuente.origenScopus
dc.identifier.doi10.1214/22-EJS2002
dc.identifier.issn1935-7524
dc.identifier.scopusidSCOPUS_ID:85128416045
dc.identifier.urihttp://doi.org/10.1214/22-EJS2002
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86856
dc.identifier.wosidWOS:000825293500045
dc.information.autorucFacultad de Matemáticas; Jara Vallejos, Alejandro Antonio; 0000-0002-2282-353X; 127927
dc.issue.numero1
dc.language.isoen
dc.nota.accesocontenido completo
dc.pagina.final2405
dc.pagina.inicio2346
dc.publisherInstitute of Mathematical Statistics
dc.relation.ispartofElectronic Journal of Statistics
dc.revistaElectronic Journal of Statistics
dc.rightsacceso abierto
dc.rights.licenseATTRIBUTION 4.0 INTERNATIONAL
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectDensity regression
dc.subjectdependent Dirichlet processes
dc.subjectDirichlet process
dc.subjectFully nonparametric regression
dc.subject.ddc510
dc.subject.deweyMatemática física y químicaes_ES
dc.titleDependent Bayesian nonparametric modeling of compositional data using random Bernstein polynomials
dc.typeartículo
dc.volumen16
sipa.codpersvinculados127927
sipa.trazabilidadSCOPUS;2022-07-08
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