Empirical non-parametric control charts: estimation effects and corrections

Willem Albers, Wilbert C.M. Kallenberg

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    40 Citations (Scopus)


    Owing to the extreme quantiles involved, standard control charts are very sensitive to the effects of parameter estimation and non-normality. More general parametric charts have been devised to deal with the latter complication and corrections have been derived to compensate for the estimation step, both under normal and parametric models. The resulting procedures offer a satisfactory solution over a broad range of underlying distributions. However, situations do occur where even such a large model is inadequate and nothing remains but to consider nonparametric charts. In principle, these form ideal solutions, but the problem is that huge sample sizes are required for the estimation step. Otherwise the resulting stochastic error is so large that the chart is very unstable, a disadvantage that seems to outweigh the advantage of avoiding the model error from the parametric case. Here we analyse under what conditions non-parametric charts actually become feasible alternatives for their parametric counterparts. In particular, corrected versions are suggested for which a possible change point is reached at sample sizes that are markedly less huge (but still larger than the customary range). These corrections serve to control the behaviour during in-control (markedly wrong outcomes of the estimates only occur sufficiently rarely). The price for this protection will clearly be some loss of detection power during out-of-control. A change point comes in view as soon as this loss can be made sufficiently small.
    Original languageEnglish
    Pages (from-to)345-360
    Number of pages16
    JournalJournal of applied statistics
    Issue number3
    Publication statusPublished - 2004


    • MSC-62G15
    • MSC-62P30
    • Statistical process control
    • Empirical quantiles
    • Phase II control limits
    • Exceedance probability

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