Robust Monitoring of Multivariate Location Using a New Nonparametric Depth‐Based Control Chart.
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| Title: | Robust Monitoring of Multivariate Location Using a New Nonparametric Depth‐Based Control Chart. |
|---|---|
| Authors: | Bayati, Mahdieh1 (AUTHOR), Nasrollahzadeh, Shadi2 (AUTHOR) sh_nasrollahzadeh@iau.ir, Bameni Moghadam, Mohammad3 (AUTHOR), Venetis, John (AUTHOR) johnvenetis4@gmail.com |
| Source: | Journal of Applied Mathematics. 7/3/2026, Vol. 2026, p1-13. 13p. |
| Subjects: | Statistical process control, Quality control charts |
| Abstract: | Monitoring multivariate processes under nonnormal conditions remains a persistent challenge in statistical process control, as most classical control charts rely heavily on distributional assumptions. To address this limitation, this paper proposes a depth‐based Shewhart‐type control chart, denoted by LD2, which detects shifts in multivariate location through discrepancies in data‐depth profiles. The proposed framework is implemented using three widely studied depth functions—Mahalanobis, halfspace, and simplicial depth—and its performance is evaluated in comparison with Hotelling′s T2 and Randles′ Rn charts. Extensive Monte Carlo simulations under normal, heavy‐tailed, and light‐tailed distributions indicate that the LD2 chart maintains stable in‐control performance while providing improved sensitivity to small and moderate shifts, particularly in the presence of skewness or heavy tails. Among the depth functions considered, the simplicial‐depth‐based version consistently demonstrates superior detection capability in non‐Gaussian settings. The practical effectiveness of the proposed method is illustrated through a real‐data application to wine‐quality monitoring, where the LD2 chart identifies a larger proportion of out‐of‐control subgroups compared with classical approaches. These results suggest that LD2 provides a flexible and robust alternative for multivariate process monitoring when standard normality assumptions are violated. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Applied Mathematics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 195124664 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Robust Monitoring of Multivariate Location Using a New Nonparametric Depth‐Based Control Chart. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Bayati%2C+Mahdieh%22">Bayati, Mahdieh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nasrollahzadeh%2C+Shadi%22">Nasrollahzadeh, Shadi</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sh_nasrollahzadeh@iau.ir</i><br /><searchLink fieldCode="AR" term="%22Bameni+Moghadam%2C+Mohammad%22">Bameni Moghadam, Mohammad</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Venetis%2C+John%22">Venetis, John</searchLink> (AUTHOR)<i> johnvenetis4@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Applied+Mathematics%22">Journal of Applied Mathematics</searchLink>. 7/3/2026, Vol. 2026, p1-13. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Statistical+process+control%22">Statistical process control</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+control+charts%22">Quality control charts</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Monitoring multivariate processes under nonnormal conditions remains a persistent challenge in statistical process control, as most classical control charts rely heavily on distributional assumptions. To address this limitation, this paper proposes a depth‐based Shewhart‐type control chart, denoted by LD2, which detects shifts in multivariate location through discrepancies in data‐depth profiles. The proposed framework is implemented using three widely studied depth functions—Mahalanobis, halfspace, and simplicial depth—and its performance is evaluated in comparison with Hotelling′s T2 and Randles′ Rn charts. Extensive Monte Carlo simulations under normal, heavy‐tailed, and light‐tailed distributions indicate that the LD2 chart maintains stable in‐control performance while providing improved sensitivity to small and moderate shifts, particularly in the presence of skewness or heavy tails. Among the depth functions considered, the simplicial‐depth‐based version consistently demonstrates superior detection capability in non‐Gaussian settings. The practical effectiveness of the proposed method is illustrated through a real‐data application to wine‐quality monitoring, where the LD2 chart identifies a larger proportion of out‐of‐control subgroups compared with classical approaches. These results suggest that LD2 provides a flexible and robust alternative for multivariate process monitoring when standard normality assumptions are violated. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Applied Mathematics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=195124664 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/jama/2550562 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Statistical process control Type: general – SubjectFull: Quality control charts Type: general Titles: – TitleFull: Robust Monitoring of Multivariate Location Using a New Nonparametric Depth‐Based Control Chart. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Bayati, Mahdieh – PersonEntity: Name: NameFull: Nasrollahzadeh, Shadi – PersonEntity: Name: NameFull: Bameni Moghadam, Mohammad – PersonEntity: Name: NameFull: Venetis, John IsPartOfRelationships: – BibEntity: Dates: – D: 03 M: 07 Text: 7/3/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1110757X Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Journal of Applied Mathematics Type: main |
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