Distinguishing between mean, variance and autocorrelation changes in statistical quality control

Y. Guo, K. J. Dooley

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


In order to enhance the probability of correct quality diagnosis, it is useful to be able to identify the statistical manner in which the quality signal has changed, i.e. identify change structure. Specifically we wish to distinguish between changes in mean, variance and lag one autocorrelation. Because these change structures yield significant similarities in their corresponding output, a multistage decision tree is necessary. A multistage classification system with a neural network and quadratic discriminant functions is used, where neural network output is an a priori distribution for the Bayesian quadratic discriminant function. Experimental results show that this multistage decision strategy performs significantly better than its single stage counterpart, with an overall success rate of 84%.

Original languageEnglish (US)
Pages (from-to)497-510
Number of pages14
JournalInternational Journal of Production Research
Issue number2
StatePublished - Feb 1995
Externally publishedYes

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Distinguishing between mean, variance and autocorrelation changes in statistical quality control'. Together they form a unique fingerprint.

Cite this