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Frontiers in Statistical Quality Control 9 by Sven Knoth (auth.), Hans-Joachim Lenz, Peter-Theodor

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By Sven Knoth (auth.), Hans-Joachim Lenz, Peter-Theodor Wilrich, Wolfgang Schmid (eds.)

The twenty-three papers during this quantity are rigorously chosen, reviewed and revised for this quantity, and are divided into components: half 1: "On-line keep watch over" with subchapters 1.1 "Control Charts" and 1.2 "Surveillance Sampling and Sampling Plans" and half 2:"Off-line Control".

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2005). " IIE Transactions, 37(11), 971-982. Zimmer, L. , Montgomery, D. , and Runger, G. C. (2000). " International Journal of Production Research, 38(9), 1977-1992. Multivariate Monitoring of the Process Mean and Variability Using Combinations of Shewhart and MEWMA Control Charts Marion R. 1, and Zachary G. edu 2 Rutgers, The State University of New Jersey, Piscataway, NJ 08854-8054, USA Summary. Control charts are considered for the problem of simultaneously monitoring the mean and variability of a multivariate process when the joint distribution of the process variables is multivariate normal.

Thus, by utilizing the concept of unbiased ARL function the potential control chart user would get a nearly symmetric scheme for all considered charts. The question remains open whether it is reasonable to ask for a control chart design that provides ARL performance symmetric in σ = σ0 . The results obtained here and their practical meaning will be summarized in the next section. 4 Conclusions It was and is quite popular to deploy the log transformation in order to get an appropriate symmetric control chart for monitoring normal variance.

Profiles reflect functional relationships between a response variable and one or more explanatory variables. Compared with a multivariate dataset, profiles contain even more data point and have to be modeled as an extremely high dimensional problem. Both model-based and model-free methods have been proposed for profile monitoring (see Woodall et al. (2004) for an extensive survey). As a mathematical tool in signal processing, wavelet transformation has been applied to filter and decompose profile-type variables or quality characteristics (Ganesan et al.

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