Estimation of the conformance fraction in a presence of misclassification errors: a

Detalhes bibliográficos
Autor(a) principal: Quinino, Roberto da Costa
Data de Publicação: 2010
Outros Autores: Pires, Magda Carvalho, Suyama, Emilio, Ho, Linda Lee
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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spelling 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
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