A likelihood-ratio-based EWMA control chart for monitoring variability of multivariate normal processes

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A multivariate EWMA control chart for monitoring process variability with individual observations (2005, methodology): “Yeh et al. (2004), using the unbiased likelihood ratio test for testing H0: Σ = Σ0, versus Ha: Σ = Σ0, developed a multivariate EWMA control chart to detect general changes in Σ0.”
Monitoring the covariance matrix via penalized likelihood estimation (2013, methodology): “…in a covariance matrix by examining the likelihood ratio, the statistic W in Equation (1) and other modified versions of it have been used by many authors to develop control charts for monitoring the covariance matrix (see, for example, Yeh et al. (2004) and Hawkins and MaboudouTchao (2008)).”
Using vector autoregressive residuals to monitor multivariate processes in the presence of serial correlation (2007, methodology): “The likelihood ratiobased method proposed in Yeh et al. (2004) can be used in monitoring the changes in multivariate variability.”
Multivariate EWMA control charts using individual observations for process mean and variance monitoring and diagnosis (2008, methodology): “( 2004 ) apply a likelihood-ratio-based EWMA control chart for monitoring the variability of multivariate normal processes.”