Multipartition model for multiple change point identification

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dc.contributor.authorPedroso, Ricardo C.
dc.contributor.authorLoschi, Rosangela H.
dc.contributor.authorQuintana, Fernando Andres
dc.date.accessioned2025-01-20T20:15:47Z
dc.date.available2025-01-20T20:15:47Z
dc.date.issued2023
dc.description.abstractThe product partition model (PPM) is widely used for detecting multiple change points. Because changes in different parameters may occur at different times, the PPM fails to identify which parameters experienced the changes. To solve this limitation, we introduce a multipartition model to detect multiple change points occurring in several parameters. It assumes that changes experienced by each parameter generate a different random partition along the time axis, which facilitates identifying those parameters that changed and the time when they do so. We apply our model to detect multiple change points in Normal means and variances. Simulations and data illustrations show that the proposed model is competitive and enriches the analysis of change point problems.
dc.fuente.origenWOS
dc.identifier.doi10.1007/s11749-023-00851-4
dc.identifier.eissn1863-8260
dc.identifier.issn1133-0686
dc.identifier.urihttps://doi.org/10.1007/s11749-023-00851-4
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/92283
dc.identifier.wosidWOS:000965555300001
dc.issue.numero2
dc.language.isoen
dc.pagina.final783
dc.pagina.inicio759
dc.revistaTest
dc.rightsacceso restringido
dc.subjectRandom partitions
dc.subjectProduct distribution
dc.subjectTemporal cluster
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleMultipartition model for multiple change point identification
dc.typeartículo
dc.volumen32
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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