KGsurv: an R package to fit the Kumaraswamy-G family of distributions for survival data
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFMG |
Texto Completo: | http://hdl.handle.net/1843/39046 https://orcid.org/0000-0003-2332-4457 |
Resumo: | In this work, a new package in language R (called KGsurv) was proposed using algorithms implemented in Stan software, to model new models based on the Kumaraswamy-G family of distributions. We present the Kumaraswamy-G regression models for the following classes: proportional hazards, proportional odds, and accelerated failure time considering Exponential, Weibull, Gamma, Log-logistics, and Log-normal distributions to model the G distribution. In this work, the frequentist approach was considered to estimate the parameters of the models with right-censored survival data under the assumption of a non-informative censoring mechanism. Finally, applications were presented using three real data sets widely used in the survival analysis literature to verify the results of the models implemented in the KGsurv package. |
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Fábio Nogueira Demarquihttp://lattes.cnpq.br/2746210170266413Cristiano de Carvalho SantosEnrico Antônio ColosimoJeremias da Silva Leãohttp://lattes.cnpq.br/7155498703018831Caio Gabriel Barreto Balieiro2022-01-08T02:55:18Z2022-01-08T02:55:18Z2021-05-13http://hdl.handle.net/1843/39046https://orcid.org/0000-0003-2332-4457In this work, a new package in language R (called KGsurv) was proposed using algorithms implemented in Stan software, to model new models based on the Kumaraswamy-G family of distributions. We present the Kumaraswamy-G regression models for the following classes: proportional hazards, proportional odds, and accelerated failure time considering Exponential, Weibull, Gamma, Log-logistics, and Log-normal distributions to model the G distribution. In this work, the frequentist approach was considered to estimate the parameters of the models with right-censored survival data under the assumption of a non-informative censoring mechanism. Finally, applications were presented using three real data sets widely used in the survival analysis literature to verify the results of the models implemented in the KGsurv package.Neste trabalho, foi proposto um novo pacote em linguaguem R (denominado KGsurv) utilizando algoritmos implementados no software Stan, para modelar novos modelos baseados na família de distribuições Kumaraswamy-G. Nós apresentamos os modelos de regressão Kumaraswamy-G para as seguintes classes: riscos proporcionais, chances proporcionais e tempo de vida acelerado considerando as distribuições Exponencial, Weibull, Gamma, Log-logística, e Log-normal para modelar a distribuição de G. Neste trabalho, a abordagem frequentista foi considerada para estimar os parâmetros dos modelos com dados de sobrevivência censurados à direita sob o pressuposto de um mecanismo de censura não informativo. Por fim, foram apresentadas aplicações utilizando três conjuntos de dados reais já utilizados na literatura de análise de sobrevivência para verificar os resultados dos modelos implementados no pacote KGsurvFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas GeraisCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorengUniversidade Federal de Minas GeraisPrograma de Pós-Graduação em EstatísticaUFMGBrasilICX - DEPARTAMENTO DE ESTATÍSTICAEstatística – TesesAnálise de sobrevivência (Biometria) – TesesAnálise de regressão – TesesKumaraswamy-Gsurvival analysisregression modelsKGsurv package.KGsurv: an R package to fit the Kumaraswamy-G family of distributions for survival dataKGsurv: um pacote R para a família de distribuições Kumaraswamy-G para dados de sobrevivênciainfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGORIGINALDissertação_Caio_Balieiro.pdfDissertação_Caio_Balieiro.pdfapplication/pdf1666745https://repositorio.ufmg.br/bitstream/1843/39046/1/Disserta%c3%a7%c3%a3o_Caio_Balieiro.pdff6f185da922fdd9a1cd6bec5a8897305MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-82118https://repositorio.ufmg.br/bitstream/1843/39046/2/license.txtcda590c95a0b51b4d15f60c9642ca272MD521843/390462022-01-07 23:55:19.094oai:repositorio.ufmg.br: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ório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2022-01-08T02:55:19Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false |
dc.title.pt_BR.fl_str_mv |
KGsurv: an R package to fit the Kumaraswamy-G family of distributions for survival data |
dc.title.alternative.pt_BR.fl_str_mv |
KGsurv: um pacote R para a família de distribuições Kumaraswamy-G para dados de sobrevivência |
title |
KGsurv: an R package to fit the Kumaraswamy-G family of distributions for survival data |
spellingShingle |
KGsurv: an R package to fit the Kumaraswamy-G family of distributions for survival data Caio Gabriel Barreto Balieiro Kumaraswamy-G survival analysis regression models KGsurv package. Estatística – Teses Análise de sobrevivência (Biometria) – Teses Análise de regressão – Teses |
title_short |
KGsurv: an R package to fit the Kumaraswamy-G family of distributions for survival data |
title_full |
KGsurv: an R package to fit the Kumaraswamy-G family of distributions for survival data |
title_fullStr |
KGsurv: an R package to fit the Kumaraswamy-G family of distributions for survival data |
title_full_unstemmed |
KGsurv: an R package to fit the Kumaraswamy-G family of distributions for survival data |
title_sort |
KGsurv: an R package to fit the Kumaraswamy-G family of distributions for survival data |
author |
Caio Gabriel Barreto Balieiro |
author_facet |
Caio Gabriel Barreto Balieiro |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Fábio Nogueira Demarqui |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/2746210170266413 |
dc.contributor.referee1.fl_str_mv |
Cristiano de Carvalho Santos |
dc.contributor.referee2.fl_str_mv |
Enrico Antônio Colosimo |
dc.contributor.referee3.fl_str_mv |
Jeremias da Silva Leão |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/7155498703018831 |
dc.contributor.author.fl_str_mv |
Caio Gabriel Barreto Balieiro |
contributor_str_mv |
Fábio Nogueira Demarqui Cristiano de Carvalho Santos Enrico Antônio Colosimo Jeremias da Silva Leão |
dc.subject.por.fl_str_mv |
Kumaraswamy-G survival analysis regression models KGsurv package. |
topic |
Kumaraswamy-G survival analysis regression models KGsurv package. Estatística – Teses Análise de sobrevivência (Biometria) – Teses Análise de regressão – Teses |
dc.subject.other.pt_BR.fl_str_mv |
Estatística – Teses Análise de sobrevivência (Biometria) – Teses Análise de regressão – Teses |
description |
In this work, a new package in language R (called KGsurv) was proposed using algorithms implemented in Stan software, to model new models based on the Kumaraswamy-G family of distributions. We present the Kumaraswamy-G regression models for the following classes: proportional hazards, proportional odds, and accelerated failure time considering Exponential, Weibull, Gamma, Log-logistics, and Log-normal distributions to model the G distribution. In this work, the frequentist approach was considered to estimate the parameters of the models with right-censored survival data under the assumption of a non-informative censoring mechanism. Finally, applications were presented using three real data sets widely used in the survival analysis literature to verify the results of the models implemented in the KGsurv package. |
publishDate |
2021 |
dc.date.issued.fl_str_mv |
2021-05-13 |
dc.date.accessioned.fl_str_mv |
2022-01-08T02:55:18Z |
dc.date.available.fl_str_mv |
2022-01-08T02:55:18Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1843/39046 |
dc.identifier.orcid.pt_BR.fl_str_mv |
https://orcid.org/0000-0003-2332-4457 |
url |
http://hdl.handle.net/1843/39046 https://orcid.org/0000-0003-2332-4457 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Estatística |
dc.publisher.initials.fl_str_mv |
UFMG |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
ICX - DEPARTAMENTO DE ESTATÍSTICA |
publisher.none.fl_str_mv |
Universidade Federal de Minas Gerais |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFMG instname:Universidade Federal de Minas Gerais (UFMG) instacron:UFMG |
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Universidade Federal de Minas Gerais (UFMG) |
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UFMG |
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UFMG |
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Repositório Institucional da UFMG |
collection |
Repositório Institucional da UFMG |
bitstream.url.fl_str_mv |
https://repositorio.ufmg.br/bitstream/1843/39046/1/Disserta%c3%a7%c3%a3o_Caio_Balieiro.pdf https://repositorio.ufmg.br/bitstream/1843/39046/2/license.txt |
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