Behaviour of resampling methods under different weighting schemes, measures and variable resampling strengths

Autores
Kopuchian, Cecilia; Ramirez, Martin Javier
Año de publicación
2010
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
We compared general behaviour trends of resampling methods (bootstrap, bootstrap with Poisson distribution, jackknife, and jackknife with symmetric resampling) and different ways to summarize the results for resampling (absolute frequency, F, and frequency difference, GC¢) for real data sets under variable resampling strengths in three weighting schemes. We propose an equivalence between bootstrap and jackknife in order to make bootstrap variable across different resampling strengths. Specifically, for each method we evaluated the number of spurious groups (groups not present in the strict consensus of the unaltered data set), of real groups, and of inconsistencies in ranking of groups under variable resampling strengths. We found that GC¢ always generated more spurious groups and recovered more groups than F. Bootstrap methods generated more spurious groups than jackknife methods; and jackknife is the method that recovered more real groups. We consistently obtained a higher proportion of spurious groups for GC¢ than for F; and for bootstrap than for jackknife. Finally, we evaluated the ranking of groups under variable resampling strengths qualitatively in the trajectories of ‘‘support’’ against resampling strength, and quantitatively with Kendall coefficient values. We found fewer ranking inconsistencies for GC¢ than for F, and for bootstrap than for jackknife.
Fil: Kopuchian, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Centro de Ecología Aplicada del Litoral. Universidad Nacional del Nordeste. Centro de Ecología Aplicada del Litoral; Argentina
Fil: Ramirez, Martin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; Argentina
Materia
JAKKNIFE
BOOTSTRAP
SUPPORT
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:11336/150462
Acceder
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spelling Behaviour of resampling methods under different weighting schemes, measures and variable resampling strengthsKopuchian, CeciliaRamirez, Martin JavierJAKKNIFEBOOTSTRAPSUPPORThttps://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1We compared general behaviour trends of resampling methods (bootstrap, bootstrap with Poisson distribution, jackknife, and jackknife with symmetric resampling) and different ways to summarize the results for resampling (absolute frequency, F, and frequency difference, GC¢) for real data sets under variable resampling strengths in three weighting schemes. We propose an equivalence between bootstrap and jackknife in order to make bootstrap variable across different resampling strengths. Specifically, for each method we evaluated the number of spurious groups (groups not present in the strict consensus of the unaltered data set), of real groups, and of inconsistencies in ranking of groups under variable resampling strengths. We found that GC¢ always generated more spurious groups and recovered more groups than F. Bootstrap methods generated more spurious groups than jackknife methods; and jackknife is the method that recovered more real groups. We consistently obtained a higher proportion of spurious groups for GC¢ than for F; and for bootstrap than for jackknife. Finally, we evaluated the ranking of groups under variable resampling strengths qualitatively in the trajectories of ‘‘support’’ against resampling strength, and quantitatively with Kendall coefficient values. We found fewer ranking inconsistencies for GC¢ than for F, and for bootstrap than for jackknife.Fil: Kopuchian, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Centro de Ecología Aplicada del Litoral. Universidad Nacional del Nordeste. Centro de Ecología Aplicada del Litoral; ArgentinaFil: Ramirez, Martin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; ArgentinaWiley Blackwell Publishing, Inc2010-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/150462Kopuchian, Cecilia; Ramirez, Martin Javier; Behaviour of resampling methods under different weighting schemes, measures and variable resampling strengths; Wiley Blackwell Publishing, Inc; Cladistics; 26; 1; 2-2010; 86-970748-3007CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/full/10.1111/j.1096-0031.2009.00269.xinfo:eu-repo/semantics/altIdentifier/doi/10.1111/j.1096-0031.2009.00269.xinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:22:40Zoai:ri.conicet.gov.ar:11336/150462instacron: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:34982025-09-10 13:22:41.102CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Behaviour of resampling methods under different weighting schemes, measures and variable resampling strengths
title Behaviour of resampling methods under different weighting schemes, measures and variable resampling strengths
spellingShingle Behaviour of resampling methods under different weighting schemes, measures and variable resampling strengths
Kopuchian, Cecilia
JAKKNIFE
BOOTSTRAP
SUPPORT
title_short Behaviour of resampling methods under different weighting schemes, measures and variable resampling strengths
title_full Behaviour of resampling methods under different weighting schemes, measures and variable resampling strengths
title_fullStr Behaviour of resampling methods under different weighting schemes, measures and variable resampling strengths
title_full_unstemmed Behaviour of resampling methods under different weighting schemes, measures and variable resampling strengths
title_sort Behaviour of resampling methods under different weighting schemes, measures and variable resampling strengths
dc.creator.none.fl_str_mv Kopuchian, Cecilia
Ramirez, Martin Javier
author Kopuchian, Cecilia
author_facet Kopuchian, Cecilia
Ramirez, Martin Javier
author_role author
author2 Ramirez, Martin Javier
author2_role author
dc.subject.none.fl_str_mv JAKKNIFE
BOOTSTRAP
SUPPORT
topic JAKKNIFE
BOOTSTRAP
SUPPORT
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.6
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv We compared general behaviour trends of resampling methods (bootstrap, bootstrap with Poisson distribution, jackknife, and jackknife with symmetric resampling) and different ways to summarize the results for resampling (absolute frequency, F, and frequency difference, GC¢) for real data sets under variable resampling strengths in three weighting schemes. We propose an equivalence between bootstrap and jackknife in order to make bootstrap variable across different resampling strengths. Specifically, for each method we evaluated the number of spurious groups (groups not present in the strict consensus of the unaltered data set), of real groups, and of inconsistencies in ranking of groups under variable resampling strengths. We found that GC¢ always generated more spurious groups and recovered more groups than F. Bootstrap methods generated more spurious groups than jackknife methods; and jackknife is the method that recovered more real groups. We consistently obtained a higher proportion of spurious groups for GC¢ than for F; and for bootstrap than for jackknife. Finally, we evaluated the ranking of groups under variable resampling strengths qualitatively in the trajectories of ‘‘support’’ against resampling strength, and quantitatively with Kendall coefficient values. We found fewer ranking inconsistencies for GC¢ than for F, and for bootstrap than for jackknife.
Fil: Kopuchian, Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Centro de Ecología Aplicada del Litoral. Universidad Nacional del Nordeste. Centro de Ecología Aplicada del Litoral; Argentina
Fil: Ramirez, Martin Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; Argentina
description We compared general behaviour trends of resampling methods (bootstrap, bootstrap with Poisson distribution, jackknife, and jackknife with symmetric resampling) and different ways to summarize the results for resampling (absolute frequency, F, and frequency difference, GC¢) for real data sets under variable resampling strengths in three weighting schemes. We propose an equivalence between bootstrap and jackknife in order to make bootstrap variable across different resampling strengths. Specifically, for each method we evaluated the number of spurious groups (groups not present in the strict consensus of the unaltered data set), of real groups, and of inconsistencies in ranking of groups under variable resampling strengths. We found that GC¢ always generated more spurious groups and recovered more groups than F. Bootstrap methods generated more spurious groups than jackknife methods; and jackknife is the method that recovered more real groups. We consistently obtained a higher proportion of spurious groups for GC¢ than for F; and for bootstrap than for jackknife. Finally, we evaluated the ranking of groups under variable resampling strengths qualitatively in the trajectories of ‘‘support’’ against resampling strength, and quantitatively with Kendall coefficient values. We found fewer ranking inconsistencies for GC¢ than for F, and for bootstrap than for jackknife.
publishDate 2010
dc.date.none.fl_str_mv 2010-02
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/11336/150462
Kopuchian, Cecilia; Ramirez, Martin Javier; Behaviour of resampling methods under different weighting schemes, measures and variable resampling strengths; Wiley Blackwell Publishing, Inc; Cladistics; 26; 1; 2-2010; 86-97
0748-3007
CONICET Digital
CONICET
url http://hdl.handle.net/11336/150462
identifier_str_mv Kopuchian, Cecilia; Ramirez, Martin Javier; Behaviour of resampling methods under different weighting schemes, measures and variable resampling strengths; Wiley Blackwell Publishing, Inc; Cladistics; 26; 1; 2-2010; 86-97
0748-3007
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/full/10.1111/j.1096-0031.2009.00269.x
info:eu-repo/semantics/altIdentifier/doi/10.1111/j.1096-0031.2009.00269.x
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
application/pdf
dc.publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
publisher.none.fl_str_mv Wiley Blackwell Publishing, Inc
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
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score 12.493442

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