Robust clustering of banks in Argentina

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Fil: Díaz, Margarita. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.

Detalles Bibliográficos
Autores principales: Díaz, Margarita, Vargas, José M., García, Fernando
Formato: conferenceObject
Lenguaje:Inglés
Publicado: 2022
Materias:
Robust clustering
Projection pursuit
Common principal
Components influence measures
Acceso en línea:http://hdl.handle.net/11086/28045
Aporte de:
Repositorio Digital Universitario (UNC) de Universidad Nacional de Córdoba
id I10-R141-11086-28045
record_format dspace
spelling I10-R141-11086-280452024-07-08T15:37:10Z Robust clustering of banks in Argentina Díaz, Margarita Vargas, José M. García, Fernando Robust clustering Projection pursuit Common principal Components influence measures 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 2022-08-07T22:54:44Z 2022-08-07T22:54:44Z 2014-10 conferenceObject http://hdl.handle.net/11086/28045 eng Licencia Creative Commons Atribución-NoComercial 4.0 Internacional http://creativecommons.org/licenses/by-nc/4.0/ Impreso
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-141
collection Repositorio Digital Universitario (UNC)
language Inglés
topic Robust clustering
Projection pursuit
Common principal
Components influence measures
spellingShingle Robust clustering
Projection pursuit
Common principal
Components influence measures
Díaz, Margarita
Vargas, José M.
García, Fernando
Robust clustering of banks in Argentina
topic_facet Robust clustering
Projection pursuit
Common principal
Components influence measures
description Fil: Díaz, Margarita. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas. Departamento de Estadística; Argentina.
format conferenceObject
author Díaz, Margarita
Vargas, José M.
García, Fernando
author_facet Díaz, Margarita
Vargas, José M.
García, Fernando
author_sort Díaz, Margarita
title Robust clustering of banks in Argentina
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
publishDate 2022
url http://hdl.handle.net/11086/28045
work_keys_str_mv AT diazmargarita robustclusteringofbanksinargentina
AT vargasjosem robustclusteringofbanksinargentina
AT garciafernando robustclusteringofbanksinargentina
_version_ 1806948546481487872

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