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QICID: 20791
Title: A Multivariate Change-Point Model for Statistical Process Control
Copyright: 2006, ASA/ASQ
Author: Zamba, K.D.; Hawkins, Douglas M.
Organization: University of Iowa, Iowa City, IA; University of Minnesota, Minneapolis, MN
Subject: Average run length (ARL),Likelihood methods,Multivariate control charts,Sample size,Statistical process control (SPC),T2 control chart,Unknown parameters;
Series: Technometrics, Vol. 48, No. 4, November 2006, pp. 539-549
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Abstract: [This abstract is based on the authors' abstract.]Traditional statistical process control charts assume that the in-control true parameters are precisely known and use these values to set the control limits. Actually, true parameter values are seldom known with exactness, but are instead estimated from a Phase I sample study that requires large samples. In addition to cost considerations, industrial settings often lack relevant data for estimating process parameters. An alternative method when monitoring for a step change in the mean vector is the unknown-parameter change-point formulation that is able to control the run behavior without the need for a large Phase I sample.
Number of pages: 11
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