Comparisons of multivariate GR&R methods using bootstrap confidence interval

Detalhes bibliográficos
Autor(a) principal: Peruchi, Rogério Santana
Data de Publicação: 2016
Outros Autores: Maciel Junior, Helio, Fernandes, Nilson José, Balestrassi, Pedro Paulo, Paiva, Anderson Paulo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Acta scientiarum. Technology (Online)
Texto Completo: http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/29294
Resumo: This paper aimed to compare the performance of multivariate GR&R (gage repeatability and reproducibility) studies based on PCA (principal component analysis) and Manova (multivariate analysis of variance) methods. To estimate the multivariate gauge index, geometric and arithmetic means have been implemented with and without weighting strategies. Bootstrap confidence interval based on BCa (bias-corrected and accelerated) method has been adopted to determine multivariate gauge index adequacy. This confidence interval was calculated for the mean of univariate gauge indices estimated from each quality characteristic. The result analyses have shown that weighted approaches provided the best estimates of gauge index in multivariate GR&R studies. 
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spelling Comparisons of multivariate GR&R methods using bootstrap confidence intervalmeasurement system analysisrepeatability and reproducibilitymultivariate analysis of varianceprincipal component analysis.Probabilidade e Estatística AplicadasThis paper aimed to compare the performance of multivariate GR&R (gage repeatability and reproducibility) studies based on PCA (principal component analysis) and Manova (multivariate analysis of variance) methods. To estimate the multivariate gauge index, geometric and arithmetic means have been implemented with and without weighting strategies. Bootstrap confidence interval based on BCa (bias-corrected and accelerated) method has been adopted to determine multivariate gauge index adequacy. This confidence interval was calculated for the mean of univariate gauge indices estimated from each quality characteristic. The result analyses have shown that weighted approaches provided the best estimates of gauge index in multivariate GR&R studies. Universidade Estadual De Maringá2016-08-26info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionanálise de sistema de medição; análise de componentes principaisapplication/pdfhttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/2929410.4025/actascitechnol.v38i4.29294Acta Scientiarum. Technology; Vol 38 No 4 (2016); 489-496Acta Scientiarum. Technology; v. 38 n. 4 (2016); 489-4961806-25631807-8664reponame:Acta scientiarum. Technology (Online)instname:Universidade Estadual de Maringá (UEM)instacron:UEMenghttp://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/29294/pdfCopyright (c) 2016 Acta Scientiarum. Technologyinfo:eu-repo/semantics/openAccessPeruchi, Rogério SantanaMaciel Junior, HelioFernandes, Nilson JoséBalestrassi, Pedro PauloPaiva, Anderson Paulo2016-08-29T11:47:11Zoai:periodicos.uem.br/ojs:article/29294Revistahttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/indexPUBhttps://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/oai||actatech@uem.br1807-86641806-2563opendoar:2016-08-29T11:47:11Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)false
dc.title.none.fl_str_mv Comparisons of multivariate GR&R methods using bootstrap confidence interval
title Comparisons of multivariate GR&R methods using bootstrap confidence interval
spellingShingle Comparisons of multivariate GR&R methods using bootstrap confidence interval
Peruchi, Rogério Santana
measurement system analysis
repeatability and reproducibility
multivariate analysis of variance
principal component analysis.
Probabilidade e Estatística Aplicadas
title_short Comparisons of multivariate GR&R methods using bootstrap confidence interval
title_full Comparisons of multivariate GR&R methods using bootstrap confidence interval
title_fullStr Comparisons of multivariate GR&R methods using bootstrap confidence interval
title_full_unstemmed Comparisons of multivariate GR&R methods using bootstrap confidence interval
title_sort Comparisons of multivariate GR&R methods using bootstrap confidence interval
author Peruchi, Rogério Santana
author_facet Peruchi, Rogério Santana
Maciel Junior, Helio
Fernandes, Nilson José
Balestrassi, Pedro Paulo
Paiva, Anderson Paulo
author_role author
author2 Maciel Junior, Helio
Fernandes, Nilson José
Balestrassi, Pedro Paulo
Paiva, Anderson Paulo
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Peruchi, Rogério Santana
Maciel Junior, Helio
Fernandes, Nilson José
Balestrassi, Pedro Paulo
Paiva, Anderson Paulo
dc.subject.por.fl_str_mv measurement system analysis
repeatability and reproducibility
multivariate analysis of variance
principal component analysis.
Probabilidade e Estatística Aplicadas
topic measurement system analysis
repeatability and reproducibility
multivariate analysis of variance
principal component analysis.
Probabilidade e Estatística Aplicadas
description This paper aimed to compare the performance of multivariate GR&R (gage repeatability and reproducibility) studies based on PCA (principal component analysis) and Manova (multivariate analysis of variance) methods. To estimate the multivariate gauge index, geometric and arithmetic means have been implemented with and without weighting strategies. Bootstrap confidence interval based on BCa (bias-corrected and accelerated) method has been adopted to determine multivariate gauge index adequacy. This confidence interval was calculated for the mean of univariate gauge indices estimated from each quality characteristic. The result analyses have shown that weighted approaches provided the best estimates of gauge index in multivariate GR&R studies. 
publishDate 2016
dc.date.none.fl_str_mv 2016-08-26
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
análise de sistema de medição; análise de componentes principais
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/29294
10.4025/actascitechnol.v38i4.29294
url http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/29294
identifier_str_mv 10.4025/actascitechnol.v38i4.29294
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv http://www.periodicos.uem.br/ojs/index.php/ActaSciTechnol/article/view/29294/pdf
dc.rights.driver.fl_str_mv Copyright (c) 2016 Acta Scientiarum. Technology
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2016 Acta Scientiarum. Technology
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Estadual De Maringá
publisher.none.fl_str_mv Universidade Estadual De Maringá
dc.source.none.fl_str_mv Acta Scientiarum. Technology; Vol 38 No 4 (2016); 489-496
Acta Scientiarum. Technology; v. 38 n. 4 (2016); 489-496
1806-2563
1807-8664
reponame:Acta scientiarum. Technology (Online)
instname:Universidade Estadual de Maringá (UEM)
instacron:UEM
instname_str Universidade Estadual de Maringá (UEM)
instacron_str UEM
institution UEM
reponame_str Acta scientiarum. Technology (Online)
collection Acta scientiarum. Technology (Online)
repository.name.fl_str_mv Acta scientiarum. Technology (Online) - Universidade Estadual de Maringá (UEM)
repository.mail.fl_str_mv ||actatech@uem.br
_version_ 1799315335957970944

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