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Some simultaneous progressive monitoring schemes for the two parameters of a zero-inflated Poisson process under unknown shifts.

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Title: Some simultaneous progressive monitoring schemes for the two parameters of a zero-inflated Poisson process under unknown shifts.
Authors: Mukherjee, Amitava1 (AUTHOR), Rakitzis, Athanasios C.2 (AUTHOR) arakitz@aegean.gr
Source: Journal of Quality Technology. 2019, Vol. 51 Issue 3, p257-283. 27p.
Subjects: Poisson processes, Light emitting diodes, Quality control charts, Statistical process control
Abstract: The zero-inflated Poisson (ZIP) distribution has been extensively studied in the literature during recent years. It is one of the most appropriate models for overdispersed data with an excessive number of zeros. Data of this type frequently arise in manufacturing processes with a low fraction of defective items. A ZIP model has two parameters; one presents the probability of extra zeros and the other stands for the expected Poisson count. In this article, we propose and study three new single control charts for detecting changes in either of the two parameters of a ZIP process. The performance of the schemes is studied via numerical simulation based on Monte-Carlo. We outline that all the existing control charts for monitoring ZIP processes are based on individual observations and assume that the shift sizes in either or both parameters are known. Our proposed schemes do not need any prior information related to shift size and can be used both for individual observations and subgroup samples. The results reveal that they are very effective in the detection of small and moderate shifts in process parameters. Practical implementation of the proposed schemes is also illustrated through an interesting real industrial data set on light emitting diodes (LED). [ABSTRACT FROM AUTHOR]
Copyright of Journal of Quality Technology is the property of Taylor & Francis Ltd 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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PubType: Academic Journal
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  Data: Some simultaneous progressive monitoring schemes for the two parameters of a zero-inflated Poisson process under unknown shifts.
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  Data: <searchLink fieldCode="AR" term="%22Mukherjee%2C+Amitava%22">Mukherjee, Amitava</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rakitzis%2C+Athanasios+C%2E%22">Rakitzis, Athanasios C.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> arakitz@aegean.gr</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Quality+Technology%22">Journal of Quality Technology</searchLink>. 2019, Vol. 51 Issue 3, p257-283. 27p.
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  Data: <searchLink fieldCode="DE" term="%22Poisson+processes%22">Poisson processes</searchLink><br /><searchLink fieldCode="DE" term="%22Light+emitting+diodes%22">Light emitting diodes</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+control+charts%22">Quality control charts</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+process+control%22">Statistical process control</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: The zero-inflated Poisson (ZIP) distribution has been extensively studied in the literature during recent years. It is one of the most appropriate models for overdispersed data with an excessive number of zeros. Data of this type frequently arise in manufacturing processes with a low fraction of defective items. A ZIP model has two parameters; one presents the probability of extra zeros and the other stands for the expected Poisson count. In this article, we propose and study three new single control charts for detecting changes in either of the two parameters of a ZIP process. The performance of the schemes is studied via numerical simulation based on Monte-Carlo. We outline that all the existing control charts for monitoring ZIP processes are based on individual observations and assume that the shift sizes in either or both parameters are known. Our proposed schemes do not need any prior information related to shift size and can be used both for individual observations and subgroup samples. The results reveal that they are very effective in the detection of small and moderate shifts in process parameters. Practical implementation of the proposed schemes is also illustrated through an interesting real industrial data set on light emitting diodes (LED). [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Quality Technology is the property of Taylor & Francis Ltd 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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      – Type: doi
        Value: 10.1080/00224065.2018.1541387
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 27
        StartPage: 257
    Subjects:
      – SubjectFull: Poisson processes
        Type: general
      – SubjectFull: Light emitting diodes
        Type: general
      – SubjectFull: Quality control charts
        Type: general
      – SubjectFull: Statistical process control
        Type: general
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      – TitleFull: Some simultaneous progressive monitoring schemes for the two parameters of a zero-inflated Poisson process under unknown shifts.
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            NameFull: Mukherjee, Amitava
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            NameFull: Rakitzis, Athanasios C.
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              Text: 2019
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