A non-default fraction bivariate regression model for credit scoring: An application to Brazilian customer data
Autor(a) principal: | |
---|---|
Data de Publicação: | 2016 |
Outros Autores: | , , |
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 |