Show simple item record

dc.rights.licensehttps://creativecommons.org/licenses/by/4.0/es_AR
dc.contributor.authorMartos Venturini, Gabrieles_AR
dc.contributor.authorHernández, Nicoláses_AR
dc.contributor.authorMuñoz, Albertoes_AR
dc.date.accessioned2023-08-16T22:07:46Z
dc.date.available2023-08-16T22:07:46Z
dc.date.issued2023
dc.identifier.urihttps://repositorio.utdt.edu/handle/20.500.13098/11992
dc.description.abstractIn this paper, we propose a novel approach to address the problem of functional outlier detection. Our method leverages a low-dimensional and stable representation of functions using Reproducing Kernel Hilbert Spaces (RKHS).We define a depth measure based on density kernels that satisfy desirable properties.We also address the challenges associated with estimating the density kernel depth. Throughout aMonte Carlo simulation we assess the performance of our functional depth measure in the outlier detection task under different scenarios. To illustrate the effectiveness of our method, we showcase the proposed method in action studying outliers in mortality rate curves.es_AR
dc.description.sponsorshipEste artículo se encuentra publicado en International Journal of Data Science and Analytics (Springer Nature)
dc.description.urihttps://doi.org/10.1007/s41060-023-00420-w
dc.format.extent8 p.es_AR
dc.format.mediumapplication/pdfes_AR
dc.languageenges_AR
dc.publisherSpringer Naturees_AR
dc.publisherInternational Journal of Data Science and Analyticses_AR
dc.rightsinfo:eu-repo/semantics/openAccesses_AR
dc.subjectFunctional Dataes_AR
dc.subjectDepth measureses_AR
dc.subjectOutlier detectiones_AR
dc.subjectMortality curveses_AR
dc.titleDensity kernel depth for outlier detection in functional dataes_AR
dc.typeinfo:eu-repo/semantics/articlees_AR
dc.type.versioninfo:eu-repo/semantics/publishedVersiones_AR


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record