Estimation of the conformance fraction in a presence of misclassification errors: a
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Operations & Production Management (Online) |
Texto Completo: | https://bjopm.org.br/bjopm/article/view/V7N1A9 |
Resumo: | This paper discusses the problem of the estimation of the proportion p when the inspection system is imperfect (subject to diagnosis errors) and the sampled items are classified repeatedly m times. One assumes that no relevant information of the prior distributions of these errors is available and consequently a posterior distribution for the proportion p with high variability is generated due to non-informative prior distributions for those errors. In this paper, the authors suggest to split randomly the sample into two subsamples. Parameters of prior distributions are estimated by the first sample and a Bayesian inferential procedure is proceeded by the second sample. Numerical results indicate that such procedure yields better performance (lower variance for the posteriori distribution) rather than a single sample of size n= n1+n2 and non-informative prior distributions for the classification errors. |
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oai:ojs.bjopm.org.br:article/68 |
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Brazilian Journal of Operations & Production Management (Online) |
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Estimation of the conformance fraction in a presence of misclassification errors: aQuality controlProportion estimationBayesian analysisBinomial distributionclassification errorsrepeated classificationsThis paper discusses the problem of the estimation of the proportion p when the inspection system is imperfect (subject to diagnosis errors) and the sampled items are classified repeatedly m times. One assumes that no relevant information of the prior distributions of these errors is available and consequently a posterior distribution for the proportion p with high variability is generated due to non-informative prior distributions for those errors. In this paper, the authors suggest to split randomly the sample into two subsamples. Parameters of prior distributions are estimated by the first sample and a Bayesian inferential procedure is proceeded by the second sample. Numerical results indicate that such procedure yields better performance (lower variance for the posteriori distribution) rather than a single sample of size n= n1+n2 and non-informative prior distributions for the classification errors. Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)2010-08-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articleapplication/pdfhttps://bjopm.org.br/bjopm/article/view/V7N1A9Brazilian Journal of Operations & Production Management; Vol. 7 No. 1 (2010): July, 2010; 181-1932237-8960reponame:Brazilian Journal of Operations & Production Management (Online)instname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROenghttps://bjopm.org.br/bjopm/article/view/V7N1A9/bjopm_68Quinino, Roberto da CostaPires, Magda CarvalhoSuyama, EmilioHo, Linda Leeinfo:eu-repo/semantics/openAccess2019-04-04T07:28:52Zoai:ojs.bjopm.org.br:article/68Revistahttps://bjopm.org.br/bjopmONGhttps://bjopm.org.br/bjopm/oaibjopm.journal@gmail.com2237-89601679-8171opendoar:2023-03-13T09:45:02.735851Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
Estimation of the conformance fraction in a presence of misclassification errors: a |
title |
Estimation of the conformance fraction in a presence of misclassification errors: a |
spellingShingle |
Estimation of the conformance fraction in a presence of misclassification errors: a Quinino, Roberto da Costa Quality control Proportion estimation Bayesian analysis Binomial distribution classification errors repeated classifications |
title_short |
Estimation of the conformance fraction in a presence of misclassification errors: a |
title_full |
Estimation of the conformance fraction in a presence of misclassification errors: a |
title_fullStr |
Estimation of the conformance fraction in a presence of misclassification errors: a |
title_full_unstemmed |
Estimation of the conformance fraction in a presence of misclassification errors: a |
title_sort |
Estimation of the conformance fraction in a presence of misclassification errors: a |
author |
Quinino, Roberto da Costa |
author_facet |
Quinino, Roberto da Costa Pires, Magda Carvalho Suyama, Emilio Ho, Linda Lee |
author_role |
author |
author2 |
Pires, Magda Carvalho Suyama, Emilio Ho, Linda Lee |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Quinino, Roberto da Costa Pires, Magda Carvalho Suyama, Emilio Ho, Linda Lee |
dc.subject.por.fl_str_mv |
Quality control Proportion estimation Bayesian analysis Binomial distribution classification errors repeated classifications |
topic |
Quality control Proportion estimation Bayesian analysis Binomial distribution classification errors repeated classifications |
description |
This paper discusses the problem of the estimation of the proportion p when the inspection system is imperfect (subject to diagnosis errors) and the sampled items are classified repeatedly m times. One assumes that no relevant information of the prior distributions of these errors is available and consequently a posterior distribution for the proportion p with high variability is generated due to non-informative prior distributions for those errors. In this paper, the authors suggest to split randomly the sample into two subsamples. Parameters of prior distributions are estimated by the first sample and a Bayesian inferential procedure is proceeded by the second sample. Numerical results indicate that such procedure yields better performance (lower variance for the posteriori distribution) rather than a single sample of size n= n1+n2 and non-informative prior distributions for the classification errors. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-08-08 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/V7N1A9 |
url |
https://bjopm.org.br/bjopm/article/view/V7N1A9 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/V7N1A9/bjopm_68 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
dc.source.none.fl_str_mv |
Brazilian Journal of Operations & Production Management; Vol. 7 No. 1 (2010): July, 2010; 181-193 2237-8960 reponame:Brazilian Journal of Operations & Production Management (Online) instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
instname_str |
Associação Brasileira de Engenharia de Produção (ABEPRO) |
instacron_str |
ABEPRO |
institution |
ABEPRO |
reponame_str |
Brazilian Journal of Operations & Production Management (Online) |
collection |
Brazilian Journal of Operations & Production Management (Online) |
repository.name.fl_str_mv |
Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
bjopm.journal@gmail.com |
_version_ |
1797051459958734848 |