On the small sample behavior of Dirichlet process mixture models for data supported on compact intervals

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dc.catalogadorgjm
dc.contributor.authorWehrhahn, Claudia
dc.contributor.authorJara Vallejos, Alejandro Antonio
dc.contributor.authorBarrientos, Andrés F.
dc.date.accessioned2024-06-26T20:23:37Z
dc.date.available2024-06-26T20:23:37Z
dc.date.issued2019
dc.description.abstractBayesian nonparametric models provide a general framework for flexible statistical modeling of modern complex data sets. We compare a rate-optimal and rate-suboptimal Bayesian nonparametric model for density estimation for data supported on a compact interval, by means of the analyses of simulated and real data. The results show that rate-optimal models are not uniformly better, across sample sizes, with respect to the way in which the posterior mass concentrates around a true model and that suboptimal models can outperform the optimal ones, even for relatively large sample sizes.
dc.fuente.origenORCID
dc.identifier.doi10.1080/03610918.2019.1568470
dc.identifier.eissn1532-4141
dc.identifier.issn0361-0918
dc.identifier.scopusid2-s2.0-85061446263
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86873
dc.identifier.wosidWOS:000623765300004
dc.information.autorucFacultad de Matemáticas; Jara Vallejos, Alejandro Antonio; 0000-0002-2282-353X; 127927
dc.issue.numero3
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final810
dc.pagina.inicio786
dc.revistaCommunications In Statistics - Simulation And Computation
dc.rightsacceso restringido
dc.subjectDensity estimation
dc.subjectRandom Bernstein polynomials
dc.subjectMixture of beta distributions
dc.subjectBayesian nonparametrics
dc.subjectPosterior convergence rate
dc.titleOn the small sample behavior of Dirichlet process mixture models for data supported on compact intervals
dc.typeartículo
dc.volumen50
sipa.codpersvinculados127927
sipa.trazabilidadORCID;2024-06-24
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