An AR-sieve bootstrap control chart for autocorrelated process data

Michelle Mancenido, Erniel Barrios

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

There are two major approaches in dealing with autocorrelated process data in process control, that is, residual-based approaches and methods that modify control limits to adjust for autocorrelation. We proposed a methodology for constructing control charts for autocorrelated process data using the AR-sieve bootstrap. The simulation study illustrates the relative advantage of the AR-sieve bootstrap control chart with respect to the in-control and out-of-control run length and false alarm rate. The proposed methodology works even for small sample sizes and conditions of the near nonstationarity of the generating process. The proposed AR-sieve bootstrap control chart presents the advantage of being distribution-free for certain class of linear models as well as the tracking of actual process observations instead of model residuals, thus facilitating the implementation during actual plant operations.

Original languageEnglish (US)
Pages (from-to)387-395
Number of pages9
JournalQuality and Reliability Engineering International
Volume28
Issue number4
DOIs
StatePublished - Jun 1 2012
Externally publishedYes

Keywords

  • AR-sieve bootstrap
  • autocorrelated process
  • distribution-free control chart

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

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