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A Bayesian approach to diagnosing covariance matrix shifts
Author(s) -
Wang Binhui,
Xu Feng,
Shu Lianjie
Publication year - 2020
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2601
Subject(s) - covariance matrix , bayesian probability , covariance , identification (biology) , computer science , multivariate statistics , matrix (chemical analysis) , process (computing) , fault detection and isolation , algorithm , pattern recognition (psychology) , artificial intelligence , mathematics , statistics , machine learning , chemistry , operating system , botany , chromatography , actuator , biology
In addition to the quick detection of abnormal changes in a multivariate process, it is also critical to provide an accurate fault identification of responsible components following an out‐of‐control signal. In line with the work of Tan and Shi for diagnosing shifts in the mean vector, this paper develops a Bayesian approach for diagnosing shifts in the covariance matrix. The simulation comparisons favor the proposed approach. A real example is also presented to demonstrate the implementation of the proposed method.