A reliable data-based smoothing parameter selection method for circular kernel estimation

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dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimizaciónes_ES
dc.contributor.authorAmeijeiras Alonso, José
dc.date.accessioned2024-05-16T14:05:04Z
dc.date.available2024-05-16T14:05:04Z
dc.date.issued2024
dc.description.abstractnew data-based smoothing parameter for circular kernel density (and its derivatives) estimation is proposed. Following the plug-in ideas, unknown quantities on an optimal smoothing parameter are replaced by suitable estimates. This paper provides a circular version of the well-known Sheather and Jones bandwidths (J R Stat Soc Ser B Stat Methodol 53(3):683–690, 1991. https://doi.org/10.1111/j.2517-6161.1991.tb01857.x), with direct and solve-the-equation plug-in rules. Theoretical support for our developments, related to the asymptotic mean squared error of the estimator of the density, its derivatives, and its functionals, for circular data, are provided. The proposed selectors are compared with previous data-based smoothing parameters for circular kernel density estimation. This paper also contributes to the study of the optimal kernel for circular data. An illustration of the proposed plug-in rules is also shown using real data on the time of car accidentses_ES
dc.description.peerreviewedSIes_ES
dc.description.sponsorshipSupported by Grant PID2020-116587GB-I00 funded by MCIN/AEI/10.13039/501100011033 and the Competitive Reference Groups 2021-2024 (ED431C 2021/24) from the Xunta de Galicia. The author is grateful to Rosa M. Crujeiras and Alberto Rodríguez-Casal for helpful suggestions and comments. The author also expresses gratitude to three anonymous reviewers for providing valuable comments that significantly contributed to the enhancement of the paper. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Naturees_ES
dc.identifier.citationAmeijeiras-Alonso, J. A reliable data-based smoothing parameter selection method for circular kernel estimation. Stat Comput 34, 73 (2024). https://doi.org/10.1007/s11222-024-10384-xes_ES
dc.identifier.doi10.1007/s11222-024-10384-x
dc.identifier.essn1573-1375
dc.identifier.issn0960-3174
dc.identifier.urihttp://hdl.handle.net/10347/33860
dc.issue.number2
dc.journal.titleStatistics and Computing
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.publisherversionhttps://doi.org/10.1007/s11222-024-10384-xes_ES
dc.rightsAtribución 4.0 Internacional
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/es_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectCircular dataes_ES
dc.subjectDirectional statisticses_ES
dc.subjectKernel density estimationes_ES
dc.subjectPlug-in rulees_ES
dc.subjectSheather and Jones bandwidthes_ES
dc.titleA reliable data-based smoothing parameter selection method for circular kernel estimationes_ES
dc.typejournal articlees_ES
dc.type.hasVersionVoRes_ES
dc.volume.number34
dspace.entity.typePublication
relation.isAuthorOfPublication0fcf8811-8071-4723-a1cb-b61c69e517b8
relation.isAuthorOfPublication.latestForDiscovery0fcf8811-8071-4723-a1cb-b61c69e517b8

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