Minimum distance method for directional data and outlier detection

Autores
Fernandez Sau, Mercedes; Rodriguez, Daniela Andrea
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
In this paper, we propose estimators based on the minimum distance for the unknown parameters of a parametric density on the unit sphere. We show that these estimators are consistent and asymptotically normally distributed. Also, we apply our proposal to develop a method that allows us to detect potential atypical values. The behavior under small samples of the proposed estimators is studied using Monte Carlo simulations. Two applications of our procedure are illustrated with real data sets.
Fil: Fernandez Sau, Mercedes. Universidad de Buenos Aires; Argentina
Fil: Rodriguez, Daniela Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
Materia
ASYMPTOTIC PROPERTIES
DIRECTIONAL DATA
OUTLIER DETECTION
ROBUST ESTIMATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/55569
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/55569
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Minimum distance method for directional data and outlier detectionFernandez Sau, MercedesRodriguez, Daniela AndreaASYMPTOTIC PROPERTIESDIRECTIONAL DATAOUTLIER DETECTIONROBUST ESTIMATIONhttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1In this paper, we propose estimators based on the minimum distance for the unknown parameters of a parametric density on the unit sphere. We show that these estimators are consistent and asymptotically normally distributed. Also, we apply our proposal to develop a method that allows us to detect potential atypical values. The behavior under small samples of the proposed estimators is studied using Monte Carlo simulations. Two applications of our procedure are illustrated with real data sets.Fil: Fernandez Sau, Mercedes. Universidad de Buenos Aires; ArgentinaFil: Rodriguez, Daniela Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; ArgentinaSpringer Verlag Berlín2017-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/55569Fernandez Sau, Mercedes; Rodriguez, Daniela Andrea; Minimum distance method for directional data and outlier detection; Springer Verlag Berlín; Advances in Data Analysis and Classification; 6-2017; 1-171862-53471862-5355CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1007/s11634-017-0287-9info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs11634-017-0287-9info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:05:15Zoai:ri.conicet.gov.ar:11336/55569instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:05:15.595CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Minimum distance method for directional data and outlier detection
title Minimum distance method for directional data and outlier detection
spellingShingle Minimum distance method for directional data and outlier detection
Fernandez Sau, Mercedes
ASYMPTOTIC PROPERTIES
DIRECTIONAL DATA
OUTLIER DETECTION
ROBUST ESTIMATION
title_short Minimum distance method for directional data and outlier detection
title_full Minimum distance method for directional data and outlier detection
title_fullStr Minimum distance method for directional data and outlier detection
title_full_unstemmed Minimum distance method for directional data and outlier detection
title_sort Minimum distance method for directional data and outlier detection
dc.creator.none.fl_str_mv Fernandez Sau, Mercedes
Rodriguez, Daniela Andrea
author Fernandez Sau, Mercedes
author_facet Fernandez Sau, Mercedes
Rodriguez, Daniela Andrea
author_role author
author2 Rodriguez, Daniela Andrea
author2_role author
dc.subject.none.fl_str_mv ASYMPTOTIC PROPERTIES
DIRECTIONAL DATA
OUTLIER DETECTION
ROBUST ESTIMATION
topic ASYMPTOTIC PROPERTIES
DIRECTIONAL DATA
OUTLIER DETECTION
ROBUST ESTIMATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv In this paper, we propose estimators based on the minimum distance for the unknown parameters of a parametric density on the unit sphere. We show that these estimators are consistent and asymptotically normally distributed. Also, we apply our proposal to develop a method that allows us to detect potential atypical values. The behavior under small samples of the proposed estimators is studied using Monte Carlo simulations. Two applications of our procedure are illustrated with real data sets.
Fil: Fernandez Sau, Mercedes. Universidad de Buenos Aires; Argentina
Fil: Rodriguez, Daniela Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; Argentina
description In this paper, we propose estimators based on the minimum distance for the unknown parameters of a parametric density on the unit sphere. We show that these estimators are consistent and asymptotically normally distributed. Also, we apply our proposal to develop a method that allows us to detect potential atypical values. The behavior under small samples of the proposed estimators is studied using Monte Carlo simulations. Two applications of our procedure are illustrated with real data sets.
publishDate 2017
dc.date.none.fl_str_mv 2017-06
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/55569
Fernandez Sau, Mercedes; Rodriguez, Daniela Andrea; Minimum distance method for directional data and outlier detection; Springer Verlag Berlín; Advances in Data Analysis and Classification; 6-2017; 1-17
1862-5347
1862-5355
CONICET Digital
CONICET
url http://hdl.handle.net/11336/55569
identifier_str_mv Fernandez Sau, Mercedes; Rodriguez, Daniela Andrea; Minimum distance method for directional data and outlier detection; Springer Verlag Berlín; Advances in Data Analysis and Classification; 6-2017; 1-17
1862-5347
1862-5355
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1007/s11634-017-0287-9
info:eu-repo/semantics/altIdentifier/url/https://link.springer.com/article/10.1007%2Fs11634-017-0287-9
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Springer Verlag Berlín
publisher.none.fl_str_mv Springer Verlag Berlín
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613886420975616
score 13.070432

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