Improved Bayes estimators and prediction for the Wilson-Hilferty distribution

Detalhes bibliográficos
Autor(a) principal: RAMOS,PEDRO L.
Data de Publicação: 2019
Outros Autores: ALMEIDA,MARCO P., TOMAZELLA,VERA L.D., LOUZADA,FRANCISCO
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Anais da Academia Brasileira de Ciências (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000500202
Resumo: Abstract: In this paper, we revisit the Wilson-Hilferty distribution and presented its mathematical properties such as the r-th moments and reliability properties. The parameters estimators are discussed using objective reference Bayesian analysis for both complete and censored data where the resulting marginal posterior intervals have accurate frequentist coverage. A simulation study is presented to compare the performance of the proposed estimators with the frequentist approach where it is observed a clear advantage for the Bayesian method. Finally, the proposed methodology is illustrated on three real datasets.
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spelling Improved Bayes estimators and prediction for the Wilson-Hilferty distributionBayesian analysisBayesian predictioncensored dataobjective priorWilson-Hilferty distribution.Abstract: In this paper, we revisit the Wilson-Hilferty distribution and presented its mathematical properties such as the r-th moments and reliability properties. The parameters estimators are discussed using objective reference Bayesian analysis for both complete and censored data where the resulting marginal posterior intervals have accurate frequentist coverage. A simulation study is presented to compare the performance of the proposed estimators with the frequentist approach where it is observed a clear advantage for the Bayesian method. Finally, the proposed methodology is illustrated on three real datasets.Academia Brasileira de Ciências2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000500202Anais da Academia Brasileira de Ciências v.91 n.3 2019reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765201920190002info:eu-repo/semantics/openAccessRAMOS,PEDRO L.ALMEIDA,MARCO P.TOMAZELLA,VERA L.D.LOUZADA,FRANCISCOeng2019-08-14T00:00:00Zoai:scielo:S0001-37652019000500202Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2019-08-14T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false
dc.title.none.fl_str_mv Improved Bayes estimators and prediction for the Wilson-Hilferty distribution
title Improved Bayes estimators and prediction for the Wilson-Hilferty distribution
spellingShingle Improved Bayes estimators and prediction for the Wilson-Hilferty distribution
RAMOS,PEDRO L.
Bayesian analysis
Bayesian prediction
censored data
objective prior
Wilson-Hilferty distribution.
title_short Improved Bayes estimators and prediction for the Wilson-Hilferty distribution
title_full Improved Bayes estimators and prediction for the Wilson-Hilferty distribution
title_fullStr Improved Bayes estimators and prediction for the Wilson-Hilferty distribution
title_full_unstemmed Improved Bayes estimators and prediction for the Wilson-Hilferty distribution
title_sort Improved Bayes estimators and prediction for the Wilson-Hilferty distribution
author RAMOS,PEDRO L.
author_facet RAMOS,PEDRO L.
ALMEIDA,MARCO P.
TOMAZELLA,VERA L.D.
LOUZADA,FRANCISCO
author_role author
author2 ALMEIDA,MARCO P.
TOMAZELLA,VERA L.D.
LOUZADA,FRANCISCO
author2_role author
author
author
dc.contributor.author.fl_str_mv RAMOS,PEDRO L.
ALMEIDA,MARCO P.
TOMAZELLA,VERA L.D.
LOUZADA,FRANCISCO
dc.subject.por.fl_str_mv Bayesian analysis
Bayesian prediction
censored data
objective prior
Wilson-Hilferty distribution.
topic Bayesian analysis
Bayesian prediction
censored data
objective prior
Wilson-Hilferty distribution.
description Abstract: In this paper, we revisit the Wilson-Hilferty distribution and presented its mathematical properties such as the r-th moments and reliability properties. The parameters estimators are discussed using objective reference Bayesian analysis for both complete and censored data where the resulting marginal posterior intervals have accurate frequentist coverage. A simulation study is presented to compare the performance of the proposed estimators with the frequentist approach where it is observed a clear advantage for the Bayesian method. Finally, the proposed methodology is illustrated on three real datasets.
publishDate 2019
dc.date.none.fl_str_mv 2019-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000500202
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000500202
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0001-3765201920190002
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Academia Brasileira de Ciências
publisher.none.fl_str_mv Academia Brasileira de Ciências
dc.source.none.fl_str_mv Anais da Academia Brasileira de Ciências v.91 n.3 2019
reponame:Anais da Academia Brasileira de Ciências (Online)
instname:Academia Brasileira de Ciências (ABC)
instacron:ABC
instname_str Academia Brasileira de Ciências (ABC)
instacron_str ABC
institution ABC
reponame_str Anais da Academia Brasileira de Ciências (Online)
collection Anais da Academia Brasileira de Ciências (Online)
repository.name.fl_str_mv Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)
repository.mail.fl_str_mv ||aabc@abc.org.br
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