Regression with Variable Dimension Covariates

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dc.contributor.authorMueller, Peter
dc.contributor.authorQuintana, Fernando Andres
dc.contributor.authorPage, Garritt L.
dc.date.accessioned2025-01-20T17:11:10Z
dc.date.available2025-01-20T17:11:10Z
dc.date.issued2024
dc.description.abstractRegression is one of the most fundamental statistical inference problems. A broad definition of regression problems is as estimation of the distribution of an outcome using a family of probability models indexed by covariates. Despite the ubiquitous nature of regression problems and the abundance of related methods and results there is a surprising gap in the literature. There are no well established methods for regression with a varying dimension covariate vectors, despite the common occurrence of such problems. In this paper we review some recent related papers proposing varying dimension regression by way of random partitions.
dc.fuente.origenWOS
dc.identifier.doi10.1007/s13171-023-00329-3
dc.identifier.eissn0976-8378
dc.identifier.issn0976-836X
dc.identifier.urihttps://doi.org/10.1007/s13171-023-00329-3
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/91164
dc.identifier.wosidWOS:001118302600001
dc.issue.numeroSUPPL 1
dc.language.isoen
dc.pagina.final198
dc.pagina.inicio185
dc.revistaSankhya-series a-mathematical statistics and probability
dc.rightsacceso restringido
dc.subjectDensity regression
dc.subjectClustering
dc.subjectPartition
dc.subjectMissing data
dc.subjectC11
dc.subjectH51
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleRegression with Variable Dimension Covariates
dc.typeartículo
dc.volumen86
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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