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Statistical Monitoring of Autocorrelated Simple Linear Profiles Based on Principal Components Analysis

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  • Seyed Taghi Akhavan Niaki
  • Majid Khedmati
  • Mir Emad Soleymanian

Abstract

In this article, a transformation method using the principal component analysis approach is first applied to remove the existing autocorrelation within each profile in Phase I monitoring of autocorrelated simple linear profiles. This easy-to-use approach is independent of the autocorrelation coefficient. Moreover, since it is a model-free method, it can be used for Phase I monitoring procedures. Then, five control schemes are proposed to monitor the parameters of the profile with uncorrelated error terms. The performances of the proposed control charts are evaluated and are compared through simulation experiments based on different values of autocorrelation coefficient as well as different shift scenarios in the parameters of the profile in terms of probability of receiving an out-of-control signal.

Suggested Citation

  • Seyed Taghi Akhavan Niaki & Majid Khedmati & Mir Emad Soleymanian, 2015. "Statistical Monitoring of Autocorrelated Simple Linear Profiles Based on Principal Components Analysis," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(21), pages 4454-4475, November.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:21:p:4454-4475
    DOI: 10.1080/03610926.2013.835417
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    Cited by:

    1. Aylin Pakzad & Ali Yeganeh & Rassoul Noorossana & Sandile Charles Shongwe, 2024. "Process Capability Index for Simple Linear Profile in the Presence of Within- and Between-Profile Autocorrelation," Mathematics, MDPI, vol. 12(16), pages 1-40, August.

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