A Bayesian EWMA method to detect jumps at the start-up phase of a process

Panagiotis Tsiamyrtzis, Douglas M. Hawkins

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


The start-up phase data of a process are the spine of traditional SPC charting and testing methods and are usually assumed to be i.i.d. observations from the in-control distribution. In this work a new method is proposed to model normally distributed start-up phase data where we allow for serial dependence and randomly occurring unidirectional level shifts of the underlying parameter of interest. The theoretic development is based on a Bayesian sequentially updated EWMA model with normal mixture errors. The new approach makes use of available prior information and provides a framework for drawing decisions and making prediction on line, even with a single observation.

Original languageEnglish (US)
Pages (from-to)721-735
Number of pages15
JournalQuality and Reliability Engineering International
Issue number6
StatePublished - Oct 2008


  • Bayesian statistical process control (SPC)
  • Correlated data
  • Normal mixtures
  • Phase I
  • Short runs


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