A non-default fraction bivariate regression model for credit scoring: An application to Brazilian customer data

Detalhes bibliográficos
Autor(a) principal: Cancho, Vicente G.
Data de Publicação: 2016
Outros Autores: Suzuki, Adriano K., Barriga, Gladys D. C. [UNESP], Louzada, Francisco
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1080/23737484.2016.1243456
http://hdl.handle.net/11449/220953
Resumo: In this article, we propose a lifetime model for bivariate survival data with a non-default rate. Our approach enables different underlying activation mechanisms that lead to the event of interest. A number of competing causes which may be responsible for the occurrence of the event of interest are assumed to follow a Poisson distributions, and a positive stable distribution was considered for the frailty component. The Markov chain Monte Carlo (MCMC) method is used in Bayesian inference approach and some Bayesian criteria are used for a comparison. Moreover, we conduct the influence diagnostic through the diagnostic measures in order to detect possible influential or extreme observations that can cause distortions on the results of the analysis. The proposed models are applied to analyze a Brazilian customer data set.
id UNSP_b0b7861740dd530216aaffd4b97decd6
oai_identifier_str oai:repositorio.unesp.br:11449/220953
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling A non-default fraction bivariate regression model for credit scoring: An application to Brazilian customer dataBivariate non-default rate modelscompeting riskscured fractionlong-term survival modelsIn this article, we propose a lifetime model for bivariate survival data with a non-default rate. Our approach enables different underlying activation mechanisms that lead to the event of interest. A number of competing causes which may be responsible for the occurrence of the event of interest are assumed to follow a Poisson distributions, and a positive stable distribution was considered for the frailty component. The Markov chain Monte Carlo (MCMC) method is used in Bayesian inference approach and some Bayesian criteria are used for a comparison. Moreover, we conduct the influence diagnostic through the diagnostic measures in order to detect possible influential or extreme observations that can cause distortions on the results of the analysis. The proposed models are applied to analyze a Brazilian customer data set.Department of Applied Mathematics and Statistics University of São PauloFaculty of Engineering at Bauru São Paulo State UniversityFaculty of Engineering at Bauru São Paulo State UniversityUniversidade de São Paulo (USP)Universidade Estadual Paulista (UNESP)Cancho, Vicente G.Suzuki, Adriano K.Barriga, Gladys D. C. [UNESP]Louzada, Francisco2022-04-28T19:07:04Z2022-04-28T19:07:04Z2016-04-02info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1-12http://dx.doi.org/10.1080/23737484.2016.1243456Communications in Statistics Case Studies Data Analysis and Applications, v. 2, n. 1-2, p. 1-12, 2016.2373-7484http://hdl.handle.net/11449/22095310.1080/23737484.2016.12434562-s2.0-85032980107Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengCommunications in Statistics Case Studies Data Analysis and Applicationsinfo:eu-repo/semantics/openAccess2022-04-28T19:07:04Zoai:repositorio.unesp.br:11449/220953Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:39:56.777409Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv A non-default fraction bivariate regression model for credit scoring: An application to Brazilian customer data
title A non-default fraction bivariate regression model for credit scoring: An application to Brazilian customer data
spellingShingle A non-default fraction bivariate regression model for credit scoring: An application to Brazilian customer data
Cancho, Vicente G.
Bivariate non-default rate models
competing risks
cured fraction
long-term survival models
title_short A non-default fraction bivariate regression model for credit scoring: An application to Brazilian customer data
title_full A non-default fraction bivariate regression model for credit scoring: An application to Brazilian customer data
title_fullStr A non-default fraction bivariate regression model for credit scoring: An application to Brazilian customer data
title_full_unstemmed A non-default fraction bivariate regression model for credit scoring: An application to Brazilian customer data
title_sort A non-default fraction bivariate regression model for credit scoring: An application to Brazilian customer data
author Cancho, Vicente G.
author_facet Cancho, Vicente G.
Suzuki, Adriano K.
Barriga, Gladys D. C. [UNESP]
Louzada, Francisco
author_role author
author2 Suzuki, Adriano K.
Barriga, Gladys D. C. [UNESP]
Louzada, Francisco
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade de São Paulo (USP)
Universidade Estadual Paulista (UNESP)
dc.contributor.author.fl_str_mv Cancho, Vicente G.
Suzuki, Adriano K.
Barriga, Gladys D. C. [UNESP]
Louzada, Francisco
dc.subject.por.fl_str_mv Bivariate non-default rate models
competing risks
cured fraction
long-term survival models
topic Bivariate non-default rate models
competing risks
cured fraction
long-term survival models
description In this article, we propose a lifetime model for bivariate survival data with a non-default rate. Our approach enables different underlying activation mechanisms that lead to the event of interest. A number of competing causes which may be responsible for the occurrence of the event of interest are assumed to follow a Poisson distributions, and a positive stable distribution was considered for the frailty component. The Markov chain Monte Carlo (MCMC) method is used in Bayesian inference approach and some Bayesian criteria are used for a comparison. Moreover, we conduct the influence diagnostic through the diagnostic measures in order to detect possible influential or extreme observations that can cause distortions on the results of the analysis. The proposed models are applied to analyze a Brazilian customer data set.
publishDate 2016
dc.date.none.fl_str_mv 2016-04-02
2022-04-28T19:07:04Z
2022-04-28T19:07:04Z
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://dx.doi.org/10.1080/23737484.2016.1243456
Communications in Statistics Case Studies Data Analysis and Applications, v. 2, n. 1-2, p. 1-12, 2016.
2373-7484
http://hdl.handle.net/11449/220953
10.1080/23737484.2016.1243456
2-s2.0-85032980107
url http://dx.doi.org/10.1080/23737484.2016.1243456
http://hdl.handle.net/11449/220953
identifier_str_mv Communications in Statistics Case Studies Data Analysis and Applications, v. 2, n. 1-2, p. 1-12, 2016.
2373-7484
10.1080/23737484.2016.1243456
2-s2.0-85032980107
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Communications in Statistics Case Studies Data Analysis and Applications
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 1-12
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808129104815325184