A new class of boundary kernels for distribution function estimation

Detalhes bibliográficos
Autor(a) principal: Tenreiro, Carlos
Data de Publicação: 2018
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/80928
https://doi.org/10.1080/03610926.2017.1390131
Resumo: In this note we introduce a new class of boundary kernels for distribution function estimation which shows itself to be especially performing when the classical kernel distribution function estimator suffers from severe boundary problems.
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spelling A new class of boundary kernels for distribution function estimationDistribution function estimation; kernel estimator; boundary kernelsIn this note we introduce a new class of boundary kernels for distribution function estimation which shows itself to be especially performing when the classical kernel distribution function estimator suffers from severe boundary problems.Taylor & Francis2018info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/80928http://hdl.handle.net/10316/80928https://doi.org/10.1080/03610926.2017.1390131enghttps://www.tandfonline.com/doi/abs/10.1080/03610926.2017.1390131?journalCode=lsta20Tenreiro, Carlosinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2020-01-23T16:24:04Zoai:estudogeral.uc.pt:10316/80928Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:03:10.136705Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A new class of boundary kernels for distribution function estimation
title A new class of boundary kernels for distribution function estimation
spellingShingle A new class of boundary kernels for distribution function estimation
Tenreiro, Carlos
Distribution function estimation; kernel estimator; boundary kernels
title_short A new class of boundary kernels for distribution function estimation
title_full A new class of boundary kernels for distribution function estimation
title_fullStr A new class of boundary kernels for distribution function estimation
title_full_unstemmed A new class of boundary kernels for distribution function estimation
title_sort A new class of boundary kernels for distribution function estimation
author Tenreiro, Carlos
author_facet Tenreiro, Carlos
author_role author
dc.contributor.author.fl_str_mv Tenreiro, Carlos
dc.subject.por.fl_str_mv Distribution function estimation; kernel estimator; boundary kernels
topic Distribution function estimation; kernel estimator; boundary kernels
description In this note we introduce a new class of boundary kernels for distribution function estimation which shows itself to be especially performing when the classical kernel distribution function estimator suffers from severe boundary problems.
publishDate 2018
dc.date.none.fl_str_mv 2018
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/80928
http://hdl.handle.net/10316/80928
https://doi.org/10.1080/03610926.2017.1390131
url http://hdl.handle.net/10316/80928
https://doi.org/10.1080/03610926.2017.1390131
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.tandfonline.com/doi/abs/10.1080/03610926.2017.1390131?journalCode=lsta20
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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