Clustering and Prediction With Variable Dimension Covariates

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dc.contributor.authorPage, Garritt L.
dc.contributor.authorQuintana, Fernando A.
dc.contributor.authorMuller, Peter
dc.date.accessioned2025-01-20T22:01:26Z
dc.date.available2025-01-20T22:01:26Z
dc.date.issued2022
dc.description.abstractIn many applied fields incomplete covariate vectors are commonly encountered. It is well known that this can be problematic when making inference on model parameters, but its impact on prediction performance is less understood. We develop a method based on covariate dependent random partition models that seamlessly handles missing covariates while completely avoiding any type of imputation. The method we develop allows in-sample as well as out-of-sample predictions, even if the missing pattern in the new subjects'incomplete covariate vectorwas not seen in the training data. Any data type, including categorical or continuous covariates are permitted. In simulation studies, the proposed method compares favorably. We illustrate themethod in two application examples. Supplementary materials for this article are available here.
dc.fuente.origenWOS
dc.identifier.doi10.1080/10618600.2021.1999824
dc.identifier.eissn1537-2715
dc.identifier.issn1061-8600
dc.identifier.urihttps://doi.org/10.1080/10618600.2021.1999824
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/93828
dc.identifier.wosidWOS:000731244600001
dc.issue.numero2
dc.language.isoen
dc.pagina.final476
dc.pagina.inicio466
dc.revistaJournal of computational and graphical statistics
dc.rightsacceso restringido
dc.subjectBayesian nonparametrics
dc.subjectDependent random partition models
dc.subjectIndicator-missing
dc.subjectPattern missing
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleClustering and Prediction With Variable Dimension Covariates
dc.typeartículo
dc.volumen31
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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