Spline local basis methods for nonparametric density estimation

UDC.coleccionInvestigaciónes_ES
UDC.departamentoMatemáticases_ES
UDC.endPage118es_ES
UDC.grupoInvModelos e Métodos Numéricos en Enxeñaría e Ciencias Aplicadas (M2NICA)es_ES
UDC.journalTitleStatistics Surveyses_ES
UDC.startPage75es_ES
UDC.volume17es_ES
dc.contributor.authorKirkby, Justin Lars
dc.contributor.authorLeitao, Álvaro
dc.contributor.authorNguyen, Duy
dc.date.accessioned2023-10-13T08:42:44Z
dc.date.available2023-10-13T08:42:44Z
dc.date.issued2023
dc.description.abstract[Abstract]: This work reviews the literature on spline local basis methods for non-parametric density estimation. Particular attention is paid to B-spline density estimators which have experienced recent advances in both theory and methodology. These estimators occupy a very interesting space in statistics, which lies aptly at the cross-section of numerous statistical frameworks. New insights, experiments, and analyses are presented to cast the various estimation concepts in a unified context, while parallels and contrasts are drawn to the more familiar contexts of kernel density estimation. Unlike kernel density estimation, the study of local basis estimation is not yet fully mature, and this work also aims to highlight the gaps in existing literature which merit further investigation.es_ES
dc.description.sponsorshipÁ. Leitao wishes to acknowledge the support received from the CITIC research centre, funded by Xunta de Galicia and the European Union (European Regional Development Fund, Galicia 2014-2020 Program) by grant ED431G 2019/01.es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.identifier.citationJ. Lars Kirkby, Álvaro Leitao, and Duy Nguyen, "Spline local basis methods for nonparametric density estimation", Statist. Surv. 17, 75 - 118, 2023. https://doi.org/10.1214/23-SS142es_ES
dc.identifier.doi10.1214/23-SS142
dc.identifier.urihttp://hdl.handle.net/2183/33761
dc.language.isoenges_ES
dc.publisherInstitute of Mathematical Statisticses_ES
dc.relation.urihttps://doi.org/10.1214/23-SS142es_ES
dc.rightsAtribución 4.0 Internacional BYes_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectWavelet estimatores_ES
dc.subjectWaveletses_ES
dc.subjectMinimax riskes_ES
dc.subjectDensity estimationes_ES
dc.titleSpline local basis methods for nonparametric density estimationes_ES
dc.typejournal articlees_ES
dspace.entity.typePublication
relation.isAuthorOfPublication537a5f9b-4679-4e65-bfa5-c15d90d5ac1c
relation.isAuthorOfPublication.latestForDiscovery537a5f9b-4679-4e65-bfa5-c15d90d5ac1c

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