Robust clustering of banks in Argentina

Autores
Díaz, Margarita; Vargas, José M.; García, Fernando
Año de publicación
2014
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Fil: Díaz, Margarita. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.
Fil: Vargas, José M. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.
Fil: Vargas, José M. Universidad Nacional de Villa María. Instituto de Ciencias Básicas y Aplicadas. Departamento de Matemáticas; Argentina.
Fil: García, Fernando. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.
The purpose of this paper is to classify and characterize 64 banks, active as of 2010 inArgentina, by means of robust techniques used on information gathered during the period 2001-2010. Based on the strategy criteria established in [Wang (2007)] and [Werbin (2010)], seven variables were selected. In agreement with bank theory, four “natural” clusters were obtained, named “Personal”, “Commercial”, “Typical and “Other banks”, using robust K-means clustering as implemented in R statistical language through the function [Kondo (2011)] detecting six outliers in the process. In order to characterize each group, projection pursuit based robust principal component analysis, [Croux (2005)], was conducted on each cluster revealing approximately a similar component structure explained by three components in excess of 80%, granting a common principal components analysis as in [Boente (2002)]. This allowed us to identify three variables which suffice for grouping and characterizing each cluster. Boente influence measures were used to detect extreme cases in the common principal components analysis.
Fil: Díaz, Margarita. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.
Fil: Vargas, José M. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.
Fil: Vargas, José M. Universidad Nacional de Villa María. Instituto de Ciencias Básicas y Aplicadas. Departamento de Matemáticas; Argentina.
Fil: García, Fernando. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.
Otras Economía y Negocios
Materia
Robust clustering
Projection pursuit
Common principal
Components influence measures
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Repositorio
Repositorio Digital Universitario (UNC)
Institución
Universidad Nacional de Córdoba
OAI Identificador
oai:rdu.unc.edu.ar:11086/28045
Acceder
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oai_identifier_str oai:rdu.unc.edu.ar:11086/28045
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repository_id_str 2572
network_name_str Repositorio Digital Universitario (UNC)
spelling Robust clustering of banks in ArgentinaDíaz, MargaritaVargas, José M.García, FernandoRobust clusteringProjection pursuitCommon principalComponents influence measuresFil: Díaz, Margarita. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.Fil: Vargas, José M. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.Fil: Vargas, José M. Universidad Nacional de Villa María. Instituto de Ciencias Básicas y Aplicadas. Departamento de Matemáticas; Argentina.Fil: García, Fernando. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.The purpose of this paper is to classify and characterize 64 banks, active as of 2010 inArgentina, by means of robust techniques used on information gathered during the period 2001-2010. Based on the strategy criteria established in [Wang (2007)] and [Werbin (2010)], seven variables were selected. In agreement with bank theory, four “natural” clusters were obtained, named “Personal”, “Commercial”, “Typical and “Other banks”, using robust K-means clustering as implemented in R statistical language through the function [Kondo (2011)] detecting six outliers in the process. In order to characterize each group, projection pursuit based robust principal component analysis, [Croux (2005)], was conducted on each cluster revealing approximately a similar component structure explained by three components in excess of 80%, granting a common principal components analysis as in [Boente (2002)]. This allowed us to identify three variables which suffice for grouping and characterizing each cluster. Boente influence measures were used to detect extreme cases in the common principal components analysis.Fil: Díaz, Margarita. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.Fil: Vargas, José M. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.Fil: Vargas, José M. Universidad Nacional de Villa María. Instituto de Ciencias Básicas y Aplicadas. Departamento de Matemáticas; Argentina.Fil: García, Fernando. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Otras Economía y Negocios2014-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://hdl.handle.net/11086/28045enginfo:eu-repo/semantics/openAccessreponame:Repositorio Digital Universitario (UNC)instname:Universidad Nacional de Córdobainstacron:UNC2025-09-29T13:41:35Zoai:rdu.unc.edu.ar:11086/28045Institucionalhttps://rdu.unc.edu.ar/Universidad públicaNo correspondehttp://rdu.unc.edu.ar/oai/snrdoca.unc@gmail.comArgentinaNo correspondeNo correspondeNo correspondeopendoar:25722025-09-29 13:41:36.272Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdobafalse
dc.title.none.fl_str_mv Robust clustering of banks in Argentina
title Robust clustering of banks in Argentina
spellingShingle Robust clustering of banks in Argentina
Díaz, Margarita
Robust clustering
Projection pursuit
Common principal
Components influence measures
title_short Robust clustering of banks in Argentina
title_full Robust clustering of banks in Argentina
title_fullStr Robust clustering of banks in Argentina
title_full_unstemmed Robust clustering of banks in Argentina
title_sort Robust clustering of banks in Argentina
dc.creator.none.fl_str_mv Díaz, Margarita
Vargas, José M.
García, Fernando
author Díaz, Margarita
author_facet Díaz, Margarita
Vargas, José M.
García, Fernando
author_role author
author2 Vargas, José M.
García, Fernando
author2_role author
author
dc.subject.none.fl_str_mv Robust clustering
Projection pursuit
Common principal
Components influence measures
topic Robust clustering
Projection pursuit
Common principal
Components influence measures
dc.description.none.fl_txt_mv Fil: Díaz, Margarita. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.
Fil: Vargas, José M. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.
Fil: Vargas, José M. Universidad Nacional de Villa María. Instituto de Ciencias Básicas y Aplicadas. Departamento de Matemáticas; Argentina.
Fil: García, Fernando. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.
The purpose of this paper is to classify and characterize 64 banks, active as of 2010 inArgentina, by means of robust techniques used on information gathered during the period 2001-2010. Based on the strategy criteria established in [Wang (2007)] and [Werbin (2010)], seven variables were selected. In agreement with bank theory, four “natural” clusters were obtained, named “Personal”, “Commercial”, “Typical and “Other banks”, using robust K-means clustering as implemented in R statistical language through the function [Kondo (2011)] detecting six outliers in the process. In order to characterize each group, projection pursuit based robust principal component analysis, [Croux (2005)], was conducted on each cluster revealing approximately a similar component structure explained by three components in excess of 80%, granting a common principal components analysis as in [Boente (2002)]. This allowed us to identify three variables which suffice for grouping and characterizing each cluster. Boente influence measures were used to detect extreme cases in the common principal components analysis.
Fil: Díaz, Margarita. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.
Fil: Vargas, José M. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.
Fil: Vargas, José M. Universidad Nacional de Villa María. Instituto de Ciencias Básicas y Aplicadas. Departamento de Matemáticas; Argentina.
Fil: García, Fernando. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.
Otras Economía y Negocios
description Fil: Díaz, Margarita. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.
publishDate 2014
dc.date.none.fl_str_mv 2014-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11086/28045
url http://hdl.handle.net/11086/28045
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Digital Universitario (UNC)
instname:Universidad Nacional de Córdoba
instacron:UNC
reponame_str Repositorio Digital Universitario (UNC)
collection Repositorio Digital Universitario (UNC)
instname_str Universidad Nacional de Córdoba
instacron_str UNC
institution UNC
repository.name.fl_str_mv Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdoba
repository.mail.fl_str_mv oca.unc@gmail.com
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score 13.070432

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