Bayesian Nonparametric Approaches for ROC Curve Inference

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dc.catalogadordfo
dc.contributor.authorCalhau Fernandes, Inacio De Carvalho Vanda
dc.contributor.authorJara, Alejandro
dc.contributor.authorBras De Carvalho, Miguel
dc.date.accessioned2024-06-26T19:29:05Z
dc.date.available2024-06-26T19:29:05Z
dc.date.issued2015
dc.description.abstractThe development of medical diagnostic tests is of great importance in clinical practice, public health, and medical research. The receiver operating characteristic (ROC) curve is a popular tool for evaluating the accuracy of such tests. We review Bayesian nonparametric methods based on Dirichlet process mixtures and the Bayesian bootstrap for ROC curve estimation and regression. The methods are illustrated by means of data concerning diagnosis of lung cancer in women.
dc.description.funderFONDECYT
dc.fuente.origenHistorial Académico
dc.identifier.citationVanda Inácio de Carvalho, Alejandro Jara, Miguel de Carvalho. Bayesian Nonparametric Approaches for ROC Curve Inference. In: Riten Mitra and Peter Mueller,editors. Nonparametric Bayesian Inference in Biostatistics. Springer; 2015. p. 327-344.
dc.identifier.eisbn978-3-319-19518-6
dc.identifier.isbn978-3-319-19517-9
dc.identifier.urihttps://link.springer.com/chapter/10.1007/978-3-319-19518-6_16
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86870
dc.identifier.wosidWOS:000376610800017
dc.information.autorucFacultad de Matemáticas; Calhau Fernandes Inacio De Carvalho Vanda; S/I; 1011562
dc.information.autorucFacultad de Matemáticas; Bras De Carvalho Miguel; S/I; 1011563
dc.language.isoen
dc.nota.accesoContenido parcial
dc.pagina.final344
dc.pagina.inicio327
dc.relation.ispartofNonparametric Bayesian Inference in Biostatistics
dc.rightsacceso restringido
dc.subject.ddc510
dc.subject.deweyMatemática física y químicaes_ES
dc.titleBayesian Nonparametric Approaches for ROC Curve Inference
dc.typecapítulo de libro
sipa.codpersvinculados1011562
sipa.codpersvinculados1011563
sipa.trazabilidadHistorial Académico;09-07-2021
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