Joint Random Partition Models for Multivariate Change Point Analysis

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dc.contributor.authorQuinlan, Jose J.
dc.contributor.authorPage, Garritt L.
dc.contributor.authorCastro, Luis M.
dc.date.accessioned2025-01-20T17:08:42Z
dc.date.available2025-01-20T17:08:42Z
dc.date.issued2024
dc.description.abstractChange point analyses are concerned with identifying positions of an ordered stochastic process that undergo abrupt local changes of some underly-ing distribution. When multiple processes are observed, it is often the case that information regarding the change point positions is shared across the different processes. This work describes a method that takes advantage of this type of infor-mation. Since the number and position of change points can be described through a partition with contiguous clusters, our approach develops a joint model for these types of partitions. We describe computational strategies associated with our ap-proach and illustrate improved performance in detecting change points through a small simulation study. We then apply our method to a financial data set of emerging markets in Latin America and highlight interesting insights discovered due to the correlation between change point locations among these economies.
dc.fuente.origenWOS
dc.identifier.doi10.1214/22-BA1344
dc.identifier.eissn1936-0975
dc.identifier.issn1931-6690
dc.identifier.urihttps://doi.org/10.1214/22-BA1344
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/90975
dc.identifier.wosidWOS:001166159300004
dc.issue.numero1
dc.language.isoen
dc.pagina.final48
dc.pagina.inicio21
dc.revistaBayesian analysis
dc.rightsacceso restringido
dc.subjectcorrelated random partitions
dc.subjectmultiple change point analysis
dc.subjectmultivariate time series
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleJoint Random Partition Models for Multivariate Change Point Analysis
dc.typeartículo
dc.volumen19
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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