An EWMA control chart for the multivariate ...
Type de document :
Article dans une revue scientifique: Article original
DOI :
URL permanente :
Titre :
An EWMA control chart for the multivariate coefficient of variation
Auteur(s) :
Giner-Bosch, Vicent [Auteur]
Universitat Politècnica de València = Universitad Politecnica de Valencia = Polytechnic University of Valencia [UPV]
Tran, Kim-Phuc [Auteur]
Ecole nationale supérieure des arts et industries textiles de Roubaix (ENSAIT)
Castagliola, Philippe [Auteur]
Systèmes Logistiques et de Production [LS2N - équipe SLP ]
Laboratoire des Sciences du Numérique de Nantes [LS2N]
Khoo, Michael Boon Chong [Auteur]
Universiti Sains Malaysia [USM]
Universitat Politècnica de València = Universitad Politecnica de Valencia = Polytechnic University of Valencia [UPV]
Tran, Kim-Phuc [Auteur]
Ecole nationale supérieure des arts et industries textiles de Roubaix (ENSAIT)
Castagliola, Philippe [Auteur]
Systèmes Logistiques et de Production [LS2N - équipe SLP ]
Laboratoire des Sciences du Numérique de Nantes [LS2N]
Khoo, Michael Boon Chong [Auteur]
Universiti Sains Malaysia [USM]
Titre de la revue :
Quality and Reliability Engineering International
Nom court de la revue :
Qual. Reliab. Eng. Int.
Numéro :
35
Pagination :
1515-1541
Date de publication :
2019-10-05
ISSN :
0748-8017
Mot(s)-clé(s) en anglais :
average run length
doubly noncentral F distribution
EWMA
multivariate coefficient of variation
Nelder-Mead method
trimmed mean
doubly noncentral F distribution
EWMA
multivariate coefficient of variation
Nelder-Mead method
trimmed mean
Discipline(s) HAL :
Sciences de l'ingénieur [physics]
Résumé en anglais : [en]
Monitoring the multivariate coefficient of variation over time is a natural choice
when the focus is on stabilising the relative variability of a multivariate process,
as is the case in a significant number of real ...
Lire la suite >Monitoring the multivariate coefficient of variation over time is a natural choice when the focus is on stabilising the relative variability of a multivariate process, as is the case in a significant number of real situations in engineering, health sciences and finance, to name but a few areas. However, not many tools are available to practitioners with this aim. This paper introduces a new control chart to monitor the multivariate coefficient of variation through an EWMA scheme. Concrete methodologies to calculate the limits and evaluate the performance of the chart proposed, and determine the optimal values of the chart’s parameters are derived, based on a theoretical study of the statistic being monitored. Computational experiments reveal that our proposal clearly outperforms existing alternatives, in terms of the average run length to detect an out-of-control state. A numerical example is included to show the efficiency of our chart when operating in practice.Lire moins >
Lire la suite >Monitoring the multivariate coefficient of variation over time is a natural choice when the focus is on stabilising the relative variability of a multivariate process, as is the case in a significant number of real situations in engineering, health sciences and finance, to name but a few areas. However, not many tools are available to practitioners with this aim. This paper introduces a new control chart to monitor the multivariate coefficient of variation through an EWMA scheme. Concrete methodologies to calculate the limits and evaluate the performance of the chart proposed, and determine the optimal values of the chart’s parameters are derived, based on a theoretical study of the statistic being monitored. Computational experiments reveal that our proposal clearly outperforms existing alternatives, in terms of the average run length to detect an out-of-control state. A numerical example is included to show the efficiency of our chart when operating in practice.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
Université de Lille
ENSAIT
Junia HEI
ENSAIT
Junia HEI
Collections :
Date de dépôt :
2023-06-20T11:15:43Z
2024-02-23T09:04:03Z
2024-02-23T09:04:03Z