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AuthorOmolofe, Olusola T.
AuthorAdegoke, Nurudeen A.
AuthorAdeoti, Olatunde A.
AuthorFasoranbaku, Olusoga A.
AuthorAbbasi, Saddam Akber
Available date2022-03-20T05:57:30Z
Publication Date2021-11-28
Publication NameQuality Technology and Quantitative Management
Identifierhttp://dx.doi.org/10.1080/16843703.2021.1948952
CitationOlusola T. Omolofe, Nurudeen A. Adegoke, Olatunde A. Adeoti, Olusoga A. Fasoranbaku & Saddam Akber Abbasi (2021): Multivariate control charts for monitoring process mean vector of individual observations under regularized covariance estimation, Quality Technology & Quantitative Management, DOI: 10.1080/16843703.2021.1948952
ISSN16843703
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85119953353&origin=inward
URIhttp://hdl.handle.net/10576/28319
AbstractMultivariate control charts are generally used in industries for monitoring and diagnosing processes characterized by several process variables. The applications of charts assume that the in-control process parameters are known and the charts’ limits are obtained from the known parameters. The parameters are typically unknown in practice, and the charts’ limits are usually based on estimated parameters from some historical in-control datasets in the Phase I study. The performance of the charts for monitoring future observation depends on efficient estimates of the process parameters from the historical in-control process. When only a few historical observations are available, the performance of the charts based on the empirical estimates of the process mean vector and covariance matrix have been shown to deviate from the desired performance of the charts based on the true parameters. We investigate the performance of the multivariate Shewhart control charts based on several shrinkage estimates of the covariance matrix when only a few in-control observations are available to estimate the parameters. Simulation results show that the control charts based on the shrinkage estimators outperform the charts based on existing classical estimators. An example involving high-dimensional monitoring is provided to illustrate the performance of the proposed Shrinkage-based Shewhart chart.
SponsorOpen Access funding provided by the Qatar National Library.
Languageen
SubjectAverage run length
control charts
covariance matrix
high-dimensional monitoring
shrinkage estimates
TitleMultivariate control charts for monitoring process mean vector of individual observations under regularized covariance estimation
TypeArticle
dc.accessType Full Text


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