On confidence intervals construction for measurement system capability indicators

Autores
Dianda, Daniela Fernanda; Pagura, José Alberto; Ballarini, Nicolás Marcelo
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
There are many criteria that have been proposed to determine the capability of a measurement system, all based on estimates of variance components. Some of them are the Precision to Tolerance Ratio, the Signal to Noise Ratio and the probabilities of misclassification.For most of these indicators, there are no exact confidence intervals, since the exact distributions of the point estimators are not known. In such situations, two approaches are widely used to obtain approximate confidence intervals: the Modified Large Samples (MLS) methods initially proposed by Graybill and Wang, and the construction of Generalized Confidence Intervals (GCI) introduced by Weerahandi.In this work we focus on the construction of the confidence intervals by the generalized approach in the context of Gauge repeatability and reproducibility studies. Since GCI are obtained by simulation procedures, we analyze the effect of the number of simulations on the variability of the confidence limits as well as the effect of the size of the experiment designed to collect data on the precision of the estimates. Both studies allowed deriving some practical implementation guidelines in the use of the GCI approach.We finally present a real case study in which this technique was applied to evaluate the capability of a destructive measurement system.
Fil: Dianda, Daniela Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias económicas y Estadística. Escuela de Estadística. Instituto de Investigaciones Teóricas y Aplicadas; Argentina
Fil: Pagura, José Alberto. Universidad Nacional de Rosario. Facultad de Ciencias económicas y Estadística. Escuela de Estadística. Instituto de Investigaciones Teóricas y Aplicadas; Argentina
Fil: Ballarini, Nicolás Marcelo. Universidad Nacional de Rosario. Facultad de Ciencias económicas y Estadística. Escuela de Estadística. Instituto de Investigaciones Teóricas y Aplicadas; Argentina
Materia
GAUGE R&R STUDIES
GENERALIZED CONFIDENCE INTERVALS
DESTRUCTIVE MEASUREMENTS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/178963
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/178963
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling On confidence intervals construction for measurement system capability indicatorsDianda, Daniela FernandaPagura, José AlbertoBallarini, Nicolás MarceloGAUGE R&R STUDIESGENERALIZED CONFIDENCE INTERVALSDESTRUCTIVE MEASUREMENTShttps://purl.org/becyt/ford/1.1https://purl.org/becyt/ford/1There are many criteria that have been proposed to determine the capability of a measurement system, all based on estimates of variance components. Some of them are the Precision to Tolerance Ratio, the Signal to Noise Ratio and the probabilities of misclassification.For most of these indicators, there are no exact confidence intervals, since the exact distributions of the point estimators are not known. In such situations, two approaches are widely used to obtain approximate confidence intervals: the Modified Large Samples (MLS) methods initially proposed by Graybill and Wang, and the construction of Generalized Confidence Intervals (GCI) introduced by Weerahandi.In this work we focus on the construction of the confidence intervals by the generalized approach in the context of Gauge repeatability and reproducibility studies. Since GCI are obtained by simulation procedures, we analyze the effect of the number of simulations on the variability of the confidence limits as well as the effect of the size of the experiment designed to collect data on the precision of the estimates. Both studies allowed deriving some practical implementation guidelines in the use of the GCI approach.We finally present a real case study in which this technique was applied to evaluate the capability of a destructive measurement system.Fil: Dianda, Daniela Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias económicas y Estadística. Escuela de Estadística. Instituto de Investigaciones Teóricas y Aplicadas; ArgentinaFil: Pagura, José Alberto. Universidad Nacional de Rosario. Facultad de Ciencias económicas y Estadística. Escuela de Estadística. Instituto de Investigaciones Teóricas y Aplicadas; ArgentinaFil: Ballarini, Nicolás Marcelo. Universidad Nacional de Rosario. Facultad de Ciencias económicas y Estadística. Escuela de Estadística. Instituto de Investigaciones Teóricas y Aplicadas; ArgentinaInternational Refereed Journal of Engineering and Science2017-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/178963Dianda, Daniela Fernanda; Pagura, José Alberto; Ballarini, Nicolás Marcelo; On confidence intervals construction for measurement system capability indicators; International Refereed Journal of Engineering and Science; International Refereed Journal of Engineering and Science; 6; 1; 1-2017; 8-162319-18212319-183XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/http://www.irjes.com/Papers/vol6-issue1/B610816.pdfinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:08:19Zoai:ri.conicet.gov.ar:11336/178963instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:08:19.323CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv On confidence intervals construction for measurement system capability indicators
title On confidence intervals construction for measurement system capability indicators
spellingShingle On confidence intervals construction for measurement system capability indicators
Dianda, Daniela Fernanda
GAUGE R&R STUDIES
GENERALIZED CONFIDENCE INTERVALS
DESTRUCTIVE MEASUREMENTS
title_short On confidence intervals construction for measurement system capability indicators
title_full On confidence intervals construction for measurement system capability indicators
title_fullStr On confidence intervals construction for measurement system capability indicators
title_full_unstemmed On confidence intervals construction for measurement system capability indicators
title_sort On confidence intervals construction for measurement system capability indicators
dc.creator.none.fl_str_mv Dianda, Daniela Fernanda
Pagura, José Alberto
Ballarini, Nicolás Marcelo
author Dianda, Daniela Fernanda
author_facet Dianda, Daniela Fernanda
Pagura, José Alberto
Ballarini, Nicolás Marcelo
author_role author
author2 Pagura, José Alberto
Ballarini, Nicolás Marcelo
author2_role author
author
dc.subject.none.fl_str_mv GAUGE R&R STUDIES
GENERALIZED CONFIDENCE INTERVALS
DESTRUCTIVE MEASUREMENTS
topic GAUGE R&R STUDIES
GENERALIZED CONFIDENCE INTERVALS
DESTRUCTIVE MEASUREMENTS
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.1
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv There are many criteria that have been proposed to determine the capability of a measurement system, all based on estimates of variance components. Some of them are the Precision to Tolerance Ratio, the Signal to Noise Ratio and the probabilities of misclassification.For most of these indicators, there are no exact confidence intervals, since the exact distributions of the point estimators are not known. In such situations, two approaches are widely used to obtain approximate confidence intervals: the Modified Large Samples (MLS) methods initially proposed by Graybill and Wang, and the construction of Generalized Confidence Intervals (GCI) introduced by Weerahandi.In this work we focus on the construction of the confidence intervals by the generalized approach in the context of Gauge repeatability and reproducibility studies. Since GCI are obtained by simulation procedures, we analyze the effect of the number of simulations on the variability of the confidence limits as well as the effect of the size of the experiment designed to collect data on the precision of the estimates. Both studies allowed deriving some practical implementation guidelines in the use of the GCI approach.We finally present a real case study in which this technique was applied to evaluate the capability of a destructive measurement system.
Fil: Dianda, Daniela Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario; Argentina. Universidad Nacional de Rosario. Facultad de Ciencias económicas y Estadística. Escuela de Estadística. Instituto de Investigaciones Teóricas y Aplicadas; Argentina
Fil: Pagura, José Alberto. Universidad Nacional de Rosario. Facultad de Ciencias económicas y Estadística. Escuela de Estadística. Instituto de Investigaciones Teóricas y Aplicadas; Argentina
Fil: Ballarini, Nicolás Marcelo. Universidad Nacional de Rosario. Facultad de Ciencias económicas y Estadística. Escuela de Estadística. Instituto de Investigaciones Teóricas y Aplicadas; Argentina
description There are many criteria that have been proposed to determine the capability of a measurement system, all based on estimates of variance components. Some of them are the Precision to Tolerance Ratio, the Signal to Noise Ratio and the probabilities of misclassification.For most of these indicators, there are no exact confidence intervals, since the exact distributions of the point estimators are not known. In such situations, two approaches are widely used to obtain approximate confidence intervals: the Modified Large Samples (MLS) methods initially proposed by Graybill and Wang, and the construction of Generalized Confidence Intervals (GCI) introduced by Weerahandi.In this work we focus on the construction of the confidence intervals by the generalized approach in the context of Gauge repeatability and reproducibility studies. Since GCI are obtained by simulation procedures, we analyze the effect of the number of simulations on the variability of the confidence limits as well as the effect of the size of the experiment designed to collect data on the precision of the estimates. Both studies allowed deriving some practical implementation guidelines in the use of the GCI approach.We finally present a real case study in which this technique was applied to evaluate the capability of a destructive measurement system.
publishDate 2017
dc.date.none.fl_str_mv 2017-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/178963
Dianda, Daniela Fernanda; Pagura, José Alberto; Ballarini, Nicolás Marcelo; On confidence intervals construction for measurement system capability indicators; International Refereed Journal of Engineering and Science; International Refereed Journal of Engineering and Science; 6; 1; 1-2017; 8-16
2319-1821
2319-183X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/178963
identifier_str_mv Dianda, Daniela Fernanda; Pagura, José Alberto; Ballarini, Nicolás Marcelo; On confidence intervals construction for measurement system capability indicators; International Refereed Journal of Engineering and Science; International Refereed Journal of Engineering and Science; 6; 1; 1-2017; 8-16
2319-1821
2319-183X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://www.irjes.com/Papers/vol6-issue1/B610816.pdf
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv International Refereed Journal of Engineering and Science
publisher.none.fl_str_mv International Refereed Journal of Engineering and Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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