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Taylor and Francis Group, Communications in Statistics - Theory and Methods, 21(44), p. 4454-4475

DOI: 10.1080/03610926.2013.835417

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

Journal article published in 2015 by Seyed Taghi Akhavan Niaki ORCID, Majid Khedmati ORCID, Mir Emad Soleymanian
This paper is available in a repository.
This paper is available in a repository.

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Abstract

In this paper, a transformation method using the principal components analysis (PCA) 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, 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.