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A Multivariate Change-Point Model for Statistical Process Control

Summary: [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.

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  • Topics: Sampling, Statistical Process Control (SPC)
  • Keywords: Average run length (ARL),Likelihood methods,Multivariate control charts,Sample size,Statistical process control (SPC),T2 control chart,Unknown parameters
  • Author: Zamba, K.D.; Hawkins, Douglas M.
  • Journal: Technometrics