10. Multivariate and Robust Procedures in Statistical Process Control

  1. James R. Thompson

Published Online: 29 NOV 2011

DOI: 10.1002/9781118109656.ch10

Empirical Model Building: Data, Models, and Reality, Second Edition

Empirical Model Building: Data, Models, and Reality, Second Edition

How to Cite

Thompson, J. R. (2011) Multivariate and Robust Procedures in Statistical Process Control, in Empirical Model Building: Data, Models, and Reality, Second Edition, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118109656.ch10

Publication History

  1. Published Online: 29 NOV 2011
  2. Published Print: 24 OCT 2011

ISBN Information

Print ISBN: 9780470467039

Online ISBN: 9781118109656

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Keywords:

  • compound test;
  • contamination model;
  • multivariate normal distribution;
  • rank test;
  • statistical process control (SPC)

Summary

This chapter develops a modeling framework for statistical process control (SPC) and then indicates the natural areas for exploration. The SPC flowchart continually monitors the output of each module and seeks to find atypical outputs at points in time that can be tracked to a particular module. This module is then considered as a candidate for immediate examination for possible suboptimalities, which can then be corrected. The SPC flowchart approach will respond rather quickly to suboptimalities. Thompson and Koronacki have proposed a compound test for SPC data in higher dimensions. The chapter compares the performance of the location rank test with that of the parametric likelihood ratio test when we have as the generator of the “in control” data a <i>p</i>-variate normal distribution with mean 0 and covariance matrix I, the identity.

Controlled Vocabulary Terms

multivariate normal distribution