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Revisiting reweighted robust standard deviation estimators for univariate Shewhart S‐charts.

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Title: Revisiting reweighted robust standard deviation estimators for univariate Shewhart S‐charts.
Authors: Mutlu, Ece Çiğdem1 (AUTHOR), Alakent, Burak1 (AUTHOR) burak.alakent@boun.edu.tr
Source: Quality & Reliability Engineering International. Jun2019, Vol. 35 Issue 4, p995-1009. 15p. 4 Charts, 4 Graphs.
Subjects: Gene expression, Crystal structure, Nanoparticles, Microstructure, Magnetic properties
Abstract: Phase I outliers, unless screened during process parameter estimation, are known to deteriorate Phase II performance of process control charts. Reweighting estimators, ie, trimming outlier subgroups and individual observations, were suggested in the literature to improve both the robustness and efficiency of the resulting parameter estimates. In the current study, effects of various reweighted estimators at different trimming levels on the Phase II performance of S‐charts are elucidated using computer simulations including isolated and mixtures of contamination models. Outlier magnitudes in the simulations are held at a moderately low level to mimic industrial practice. Subtleties, such as varying Type I error rate among different trimming levels with respect to quantiles of dispersion estimates, prevent a single method to be revealed as the best performing one under all circumstances, and choice of estimators and trimming levels should depend on the number of subgroups in Phase I and the specifics of the process. Nevertheless, S‐chart using scale M‐estimator with logistic ρ and location M‐estimator at 2% trimming generally stands out in terms of Phase II performance, and high trimming levels are particularly recommended for high number of Phase I subgroups. [ABSTRACT FROM AUTHOR]
Copyright of Quality & Reliability Engineering International 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.)
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  Data: Revisiting reweighted robust standard deviation estimators for univariate Shewhart S‐charts.
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  Data: <searchLink fieldCode="AR" term="%22Mutlu%2C+Ece+Çiğdem%22">Mutlu, Ece Çiğdem</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alakent%2C+Burak%22">Alakent, Burak</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> burak.alakent@boun.edu.tr</i>
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  Data: <searchLink fieldCode="JN" term="%22Quality+%26+Reliability+Engineering+International%22">Quality & Reliability Engineering International</searchLink>. Jun2019, Vol. 35 Issue 4, p995-1009. 15p. 4 Charts, 4 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Gene+expression%22">Gene expression</searchLink><br /><searchLink fieldCode="DE" term="%22Crystal+structure%22">Crystal structure</searchLink><br /><searchLink fieldCode="DE" term="%22Nanoparticles%22">Nanoparticles</searchLink><br /><searchLink fieldCode="DE" term="%22Microstructure%22">Microstructure</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+properties%22">Magnetic properties</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Phase I outliers, unless screened during process parameter estimation, are known to deteriorate Phase II performance of process control charts. Reweighting estimators, ie, trimming outlier subgroups and individual observations, were suggested in the literature to improve both the robustness and efficiency of the resulting parameter estimates. In the current study, effects of various reweighted estimators at different trimming levels on the Phase II performance of S‐charts are elucidated using computer simulations including isolated and mixtures of contamination models. Outlier magnitudes in the simulations are held at a moderately low level to mimic industrial practice. Subtleties, such as varying Type I error rate among different trimming levels with respect to quantiles of dispersion estimates, prevent a single method to be revealed as the best performing one under all circumstances, and choice of estimators and trimming levels should depend on the number of subgroups in Phase I and the specifics of the process. Nevertheless, S‐chart using scale M‐estimator with logistic ρ and location M‐estimator at 2% trimming generally stands out in terms of Phase II performance, and high trimming levels are particularly recommended for high number of Phase I subgroups. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Quality & Reliability Engineering International 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.)
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        Value: 10.1002/qre.2441
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      – Code: eng
        Text: English
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        PageCount: 15
        StartPage: 995
    Subjects:
      – SubjectFull: Gene expression
        Type: general
      – SubjectFull: Crystal structure
        Type: general
      – SubjectFull: Nanoparticles
        Type: general
      – SubjectFull: Microstructure
        Type: general
      – SubjectFull: Magnetic properties
        Type: general
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      – TitleFull: Revisiting reweighted robust standard deviation estimators for univariate Shewhart S‐charts.
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              Text: Jun2019
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              Y: 2019
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