Please use this identifier to cite or link to this item: http://hdl.handle.net/10662/21278
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dc.contributor.authorChacón Durán, José Enrique-
dc.contributor.authorFernández Serrano, Javier-
dc.date.accessioned2024-05-22T07:43:08Z-
dc.date.available2024-05-22T07:43:08Z-
dc.date.issued2023-
dc.identifier.urihttp://hdl.handle.net/10662/21278-
dc.descriptionOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Naturees_ES
dc.description.abstractBump hunting deals with finding in sample spaces meaningful data subsets known as bumps. These have traditionally been conceived as modal or concave regions in the graph of the underlying density function. We define an abstract bump construct based on curvature functionals of the probability density. Then, we explore several alternative characterizations involving derivatives up to second order. In particular, a suitable implementation of Good and Gaskins’ original concave bumps is proposed in the multivariate case. Moreover, we bring to exploratory data analysis concepts like the mean curvature and the Laplacian that have produced good results in applied domains. Our methodology addresses the approximation of the curvature functional with a plug-in kernel density estimator. We provide theoretical results that assure the asymptotic consistency of bump boundaries in the Hausdorff distance with affordable convergence rates. We also present asymptotically valid and consistent confidence regions bounding curvature bumps. The theory is illustrated through several use cases in sports analytics with datasets from the NBA, MLB and NFL. We conclude that the different curvature instances effectively combine to generate insightful visualizations.es_ES
dc.description.sponsorshipThe first author’s research has been supported by the MICINN grant PID2019-109387GB-I00 and the Junta de Extremadura grant GR21044es_ES
dc.format.extent25 p.es_ES
dc.format.mimetypeapplication/pdfen
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectBúsqueda de protuberanciases_ES
dc.subjectConcavidades_ES
dc.subjectEstimación de derivadases_ES
dc.subjectDensidad del kerneles_ES
dc.subjectCurvatura gaussianaes_ES
dc.subjectCurvatura mediaes_ES
dc.subjectBump huntinges_ES
dc.subjectConcavityes_ES
dc.subjectGaussian curvaturees_ES
dc.subjectKernel densityes_ES
dc.subjectDerivative estimationes_ES
dc.subjectLaplacianes_ES
dc.subjectMean curvaturees_ES
dc.titleBump hunting through density curvature featureses_ES
dc.typearticlees_ES
dc.description.versionpeerReviewedes_ES
europeana.typeTEXTen_US
dc.rights.accessRightsopenAccesses_ES
dc.subject.unesco12 Matemáticases_ES
dc.subject.unesco1209 Estadísticaes_ES
dc.subject.unesco1208.02 Teoría Analítica de la Probabilidades_ES
europeana.dataProviderUniversidad de Extremadura. Españaes_ES
dc.identifier.bibliographicCitationChacón, J.E., Fernández Serrano, J. Bump hunting through density curvature features. TEST 32, 1251–1275 (2023). https://doi.org/10.1007/s11749-023-00872-zes_ES
dc.type.versionpublishedVersiones_ES
dc.contributor.affiliationUniversidad de Extremadura. Departamento de Matemáticases_ES
dc.contributor.affiliationUniversidad Autónoma de Madrid-
dc.relation.publisherversionhttps://link.springer.com/article/10.1007/s11749-023-00872-zes_ES
dc.identifier.doi10.1007/s11749-023-00872-z-
dc.identifier.publicationtitleTESTes_ES
dc.identifier.publicationfirstpage1251es_ES
dc.identifier.publicationlastpage1275es_ES
dc.identifier.publicationvolume32es_ES
dc.identifier.e-issn1133-0686-
dc.identifier.orcid0000-0002-3675-1960es_ES
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