Semiparametric Bayesian classification with longitudinal markers

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dc.contributor.authorDe la Cruz Mesia, Rolando
dc.contributor.authorQuintana, Fernando A.
dc.contributor.authorMueller, Peter
dc.date.accessioned2024-01-10T12:08:28Z
dc.date.available2024-01-10T12:08:28Z
dc.date.issued2007
dc.description.abstractWe analyse data from a study involving 173 pregnant women. The data are observed values of the beta human chorionic gonadotropin hormone measured during the first 80 days of gestational age, including from one up to six longitudinal responses for each woman. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from data that are available at the early stages of pregnancy. We achieve the desired classification with a semiparametric hierarchical model. Specifically, we consider a Dirichlet process mixture prior for the distribution of the random effects in each group. The unknown random-effects distributions are allowed to vary across groups but are made dependent by using a design vector to select different features of a single underlying random probability measure. The resulting model is an extension of the dependent Dirichlet process model, with an additional probability model for group classification. The model is shown to perform better than an alternative model which is based on independent Dirichlet processes for the groups. Relevant posterior distributions are summarized by using Markov chain Monte Carlo methods.
dc.description.funderNCI NIH HHS
dc.description.funderNATIONAL CANCER INSTITUTE
dc.format.extent19 páginas
dc.fuente.origenWOS
dc.identifier.issn0035-9254
dc.identifier.pubmedidMEDLINE:24368871
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/76396
dc.identifier.wosidWOS:000245159600001
dc.information.autorucMedicina;De la Cruz-Mesía R;S/I;119
dc.information.autorucMatemática;Quintana F;S/I;100343
dc.language.isoen
dc.nota.accesoSin adjunto
dc.pagina.final137
dc.pagina.inicio119
dc.publisherBLACKWELL PUBLISHING
dc.revistaJOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
dc.rightsregistro bibliográfico
dc.subjectdependent non-parametric model
dc.subjectdiscriminant analysis
dc.subjectlongitudinal data
dc.subjectMarkov chain Monte Carlo sampling
dc.subjectnon-parametric modelling
dc.subjectrandom-effects models
dc.subjectspecies sampling models
dc.subjectDIRICHLET PROCESS MIXTURE
dc.subjectLINEAR MIXED MODELS
dc.subjectDISCRIMINANT-ANALYSIS
dc.subjectSAMPLING METHODS
dc.subjectPRIORS
dc.subjectDISTRIBUTIONS
dc.subjectINFERENCE
dc.subjectDENSITY
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleSemiparametric Bayesian classification with longitudinal markers
dc.typeartículo
dc.volumen56
sipa.codpersvinculados119
sipa.codpersvinculados100343
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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