Nonparametric predictive inference in statistical process control

G.R.J. Arts, F.P.A. Coolen, P. Laan, van der

    Research output: Contribution to journalArticleAcademicpeer-review

    Abstract

    Statistical process control (SPC) is used to decide when to stop a process as confidence in the quality of the next item(s) is low. Information to specify a parametric model is not always available, and as SPC is of a predictive nature, we present a control chart developed using nonparametric predictive inference. The proposed ’extrema chart’, based on the extrema of a sample of observations from the process, is a generalisation of an existing nonparametric method, which controls a process using single observations. We examine the average run length (ARL) of both the one-sided and two-sided extrema chart, and a simulation study is presented to compare the extrema chart with the well known X chart and CUSUM chart. The disadvantage of these charts is that when the process mean and variation of the in-control process have to be estimated, the ARL is biased. This is not an issue for the extrema chart, as no knowledge about the underlying distribution is required.
    Original languageEnglish
    Pages (from-to)201-216
    JournalQuality Technology & Quantitative Management
    Volume1
    Issue number2
    Publication statusPublished - 2004

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