On the Bayesian Nonparametric Generalization of IRT-Type Models

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dc.catalogadordfo
dc.contributor.authorSan Martín Gutiérrez, Ernesto Javier
dc.contributor.authorJara, Alejandro
dc.contributor.authorRolin, Jean-Marie
dc.contributor.authorMouchart, Michel
dc.date.accessioned2024-06-26T19:53:02Z
dc.date.available2024-06-26T19:53:02Z
dc.date.issued2011
dc.description.abstractWe study the identification and consistency of Bayesian semiparametric IRT-type models, where the uncertainty on the abilities' distribution is modeled using a prior distribution on the space of probability measures. We show that for the semiparametric Rasch Poisson counts model, simple restrictions ensure the identification of a general distribution generating the abilities, even for a finite number of probes. For the semiparametric Rasch model, only a finite number of properties of the general abilities' distribution can be identified by a finite number of items, which are completely characterized. The full identification of the semiparametric Rasch model can be only achieved when an infinite number of items is available. The results are illustrated using simulated data.
dc.fuente.origenConveris
dc.identifier.doi10.1007/s11336-011-9213-9
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86871
dc.identifier.wosidWOS:000292003800002
dc.information.autorucFacultad de Matemáticas; San Martin Gutierrez Ernesto Javier; 0000-0001-9812-4746; 1001052
dc.issue.numero3
dc.language.isoen
dc.nota.accesoContenido parcial
dc.pagina.final409
dc.pagina.inicio385
dc.revistaPsychometrika
dc.rightsacceso restringido
dc.subjectBayesian identification
dc.subjectBayesian consistency
dc.subjectRasch model
dc.subjectRasch Poisson counts model
dc.subjectDirichlet processes
dc.subjectPólya tree processes
dc.subject.ddc510
dc.subject.deweyMatemática física y químicaes_ES
dc.titleOn the Bayesian Nonparametric Generalization of IRT-Type Models
dc.typeartículo
dc.volumen76
sipa.codpersvinculados127927
sipa.codpersvinculados1001052
sipa.trazabilidadConveris;20-07-2021
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