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This leads to an iterative determination of the system response mean vector and covariance matrix.
Sensitivity analysis on the quantity of initial samples to estimate for in-control process mean vector and covariance matrix is also presented.
In this paper a method is proposed to conduct the economic design of the VSI T2 chart when the in-control process mean vector and covariance matrix are unknown.
In particular, the Gaussian equivalent linearization expresses the properties of an equivalent linear system in terms of the mean vector and the covariance matrix of the responses, which are the unknowns of the optimization problem in a spectral approach.
Stemming from the ideas of the generalized likelihood ratio test and the multivariate exponentially weighted moving covariance, new control charts are proposed for simultaneously monitoring the mean vector and the covariance matrix of a multivariate normal process.
where X ― and C are mean vector and covariance matrix.
Let μ and ∑ be the mean vector and covariance matrix of X.
Here and are a mean vector and an eigenvalue corresponding to the, respectively.
Gaussian distribution with zero mean vector and covariance matrix β -1 I.
The mean vector and variance covariance matrix of process are unknown.
where μ is the mean vector and ∑ is the covariance matrix.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com