Determining optimal number of samples for constructing multivariate control charts

Sheau Chiann Chen, Jeh-Nan Pan

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Normally, an average run length (ARL) is used as a measure for evaluating the detecting performance of a multivariate control chart. This has a direct impact on the false alarm cost in Phase II. In this article, we first conduct a simulation study to calculate both in-control and out-of-control ARLs under various combinations of process shifts and number of samples. Then, a trade-off analysis between sampling inspection and false alarm costs is performed. Both the simulation results and trade-off analysis suggest that the optimal number of samples for constructing a multivariate control chart in Phase I can be determined.

Original languageEnglish
Pages (from-to)228-240
Number of pages13
JournalCommunications in Statistics: Simulation and Computation
Volume40
Issue number2
DOIs
Publication statusPublished - 2011 Feb 1

Fingerprint

Multivariate Control Charts
False Alarm
Trade-offs
Average Run Length
Costs
Inspection
Simulation Study
Sampling
Calculate
Simulation
Control charts

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Statistics and Probability

Cite this

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Determining optimal number of samples for constructing multivariate control charts. / Chen, Sheau Chiann; Pan, Jeh-Nan.

In: Communications in Statistics: Simulation and Computation, Vol. 40, No. 2, 01.02.2011, p. 228-240.

Research output: Contribution to journalArticle

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