A multivariate exponentially weighted moving average control chart for monitoring process variability

By Arthur B. Yeh, Dennis K. J. Lin, Hongsheng Zhou, C. Venkataramani. 2003

390 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1503582dca7875fee240d1d56e07d25500208156

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In Statistics and Probability

Citations in the corpus

GENERALIZED VARIANCE CHART DESIGN WITH ADAPTIVE SAMPLE SIZES. THE BIVARIATE CASE (2001, methodology): “Control Chart Power improvements are even better than those obtained with the CUSUM and EWMA multivariate control charts ( [16] and [17] ), but maintaining the simplicity of Hotelling's T 2 control chart.”
A multivariate EWMA control chart for monitoring process variability with individual observations (2005, methodology): “Yeh et al . (2003) , using the idea of probability integral transformation, developed a multivariate EWMA control chart to detect changes in |Σ 0 |, the determinant of the variance-covariance matrix.”
The Performance of Multivariate Control Charts in the Presence of Measurement Error (2001, background): “The performance of the multivariate EWMA chart proposed in Lowry et al. (1992) is also directionally invariant.”
On the Monitoring of Linear Profiles (2003, methodology): “The use of a multivariate EWMA control chart, as proposed by Lowry et al. (1992), would result in better statistical performance than that of the T2 chart, but the interpretation of an out-of-control signal would still not be straightforward.”
MULTIVARIATE CUMULATIVE SUM CONTROL CHARTS BASED ON PROJECTION PURSUIT (2001, neutral): “…than the MC1, MEWMA1, MEWMA2, the combined χ 2 −MEWMA1 and the combined χ 2 −MEWMA2 charts against moderate inertia (see Appendix 1 for the definitions of these charts and note that the rough rule of thumb of Lowry et al. (1992) is used here in designing χ 2 −MEWMA1 and χ 2 −MEWMA2 charts).”
A likelihood-ratio-based EWMA control chart for monitoring variability of multivariate normal processes (2004, methodology): “…multivariate control procedures based on data depth were proposed for detecting changes in the process mean, or in the process variability or both; and Yeh et al. (2003) in which a MEWMA control chart was developed to detect specific changes in the determinant of the variance-covariance matrix.”
Using vector autoregressive residuals to monitor multivariate processes in the presence of serial correlation (2007, background): “…on multivariate quality control (process monitoring) for independent observations include Hotelling (1947), Alt (1984), Jackson (1985), Lowry et al. (1992), Wierda (1994), Mason et al. (1995), Lowry and Montgomery (1995), Aparisi and Haro (2003), Yeh et al. (2004), Villalobos et al.…”