A new class of lifetime models and the evaluation of the confidence intervals by double percentile bootstrap
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
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=S0001-37652019000100202 |
Resumo: | Abstract: In this paper, we introduce a new three-parameter distribution by compounding the Nadarajah-Haghighi and geometric distributions, which can be interpreted as a truncated Marshall-Olkin extended Weibull. The compounding procedure is based on the work by Marshall and Olkin 1997. We prove that the new distribution can be obtained as a compound model with mixing exponential distribution. It can have decreasing, increasing, upside-down bathtub, bathtub-shaped, constant and decreasing-increasing-decreasing failure rate functions depending on the values of the parameters. Some mathematical properties of the new distribution are studied including moments and quantile function. The maximum likelihood estimation procedure is discussed and a particle swarm optimization algorithm is provided for estimating the model parameters. The flexibility of the new model is illustrated with an application to a real data set. |
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Anais da Academia Brasileira de Ciências (Online) |
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A new class of lifetime models and the evaluation of the confidence intervals by double percentile bootstrapExponential distributionFailure rate functionGeometric distributionMaximum likelihood estimationNadarajah-Haghighi distribution.Abstract: In this paper, we introduce a new three-parameter distribution by compounding the Nadarajah-Haghighi and geometric distributions, which can be interpreted as a truncated Marshall-Olkin extended Weibull. The compounding procedure is based on the work by Marshall and Olkin 1997. We prove that the new distribution can be obtained as a compound model with mixing exponential distribution. It can have decreasing, increasing, upside-down bathtub, bathtub-shaped, constant and decreasing-increasing-decreasing failure rate functions depending on the values of the parameters. Some mathematical properties of the new distribution are studied including moments and quantile function. The maximum likelihood estimation procedure is discussed and a particle swarm optimization algorithm is provided for estimating the model parameters. The flexibility of the new model is illustrated with an application to a real data set.Academia Brasileira de Ciências2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000100202Anais da Academia Brasileira de Ciências v.91 n.1 2019reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765201920180480info:eu-repo/semantics/openAccessMARINHO,PEDRO R.D.BOURGUIGNON,MARCELOSILVA,RODRIGO B.CORDEIRO,GAUSS M.eng2019-04-16T00:00:00Zoai:scielo:S0001-37652019000100202Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2019-04-16T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false |
dc.title.none.fl_str_mv |
A new class of lifetime models and the evaluation of the confidence intervals by double percentile bootstrap |
title |
A new class of lifetime models and the evaluation of the confidence intervals by double percentile bootstrap |
spellingShingle |
A new class of lifetime models and the evaluation of the confidence intervals by double percentile bootstrap MARINHO,PEDRO R.D. Exponential distribution Failure rate function Geometric distribution Maximum likelihood estimation Nadarajah-Haghighi distribution. |
title_short |
A new class of lifetime models and the evaluation of the confidence intervals by double percentile bootstrap |
title_full |
A new class of lifetime models and the evaluation of the confidence intervals by double percentile bootstrap |
title_fullStr |
A new class of lifetime models and the evaluation of the confidence intervals by double percentile bootstrap |
title_full_unstemmed |
A new class of lifetime models and the evaluation of the confidence intervals by double percentile bootstrap |
title_sort |
A new class of lifetime models and the evaluation of the confidence intervals by double percentile bootstrap |
author |
MARINHO,PEDRO R.D. |
author_facet |
MARINHO,PEDRO R.D. BOURGUIGNON,MARCELO SILVA,RODRIGO B. CORDEIRO,GAUSS M. |
author_role |
author |
author2 |
BOURGUIGNON,MARCELO SILVA,RODRIGO B. CORDEIRO,GAUSS M. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
MARINHO,PEDRO R.D. BOURGUIGNON,MARCELO SILVA,RODRIGO B. CORDEIRO,GAUSS M. |
dc.subject.por.fl_str_mv |
Exponential distribution Failure rate function Geometric distribution Maximum likelihood estimation Nadarajah-Haghighi distribution. |
topic |
Exponential distribution Failure rate function Geometric distribution Maximum likelihood estimation Nadarajah-Haghighi distribution. |
description |
Abstract: In this paper, we introduce a new three-parameter distribution by compounding the Nadarajah-Haghighi and geometric distributions, which can be interpreted as a truncated Marshall-Olkin extended Weibull. The compounding procedure is based on the work by Marshall and Olkin 1997. We prove that the new distribution can be obtained as a compound model with mixing exponential distribution. It can have decreasing, increasing, upside-down bathtub, bathtub-shaped, constant and decreasing-increasing-decreasing failure rate functions depending on the values of the parameters. Some mathematical properties of the new distribution are studied including moments and quantile function. The maximum likelihood estimation procedure is discussed and a particle swarm optimization algorithm is provided for estimating the model parameters. The flexibility of the new model is illustrated with an application to a real data set. |
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=S0001-37652019000100202 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000100202 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0001-3765201920180480 |
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.1 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 |
_version_ |
1754302866954125312 |