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Attribute Charts for Zero-Inflated Processes

机译:零膨胀过程的属性图

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摘要

The classical Shewhart c-chart and p-chart which are constructed based on the Poisson and binomial distributions are inappropriate in monitoring zero-inflated counts. They tend to underestimate the dispersion of zero-inflated counts and subsequently lead to higher false alarm rate in detecting out-of-control signals. Another drawback of these charts is that their 3-sigfna control limits, evaluated based on the asymptotic normality assumption of the attribute counts, have a systematic negative bias in their coverage probability. We recommend that the zero-inflated models which account for the excess number of zeros should first be fitted to the zero-inflated Poisson and binomial counts. The Poisson parameter λ estimated from a zero-inflated Poisson model is then used to construct a one-sided c-chart with its upper control limit constructed based on the Jeffreys prior interval that provides good coverage probability for λ. Similarly, the binomial parameter p estimated from a zero-inflated binomial model is used to construct a one-sided np-chart with its upper control limit constructed based on the Jeffreys prior interval or Blyth-Still interval of the binomial proportion p. A simple two-of-two control rule is also recommended to improve further on the performance of these two proposed charts.
机译:基于泊松分布和二项式分布构造的经典Shewhart c图和p图不适用于监视零膨胀计数。它们往往会低估零膨胀计数的离散度,从而在检测失控信号时导致较高的误报率。这些图表的另一个缺点是,基于属性计数的渐近正态性假设进行评估的3-sigfna控制极限在其覆盖概率上存在系统性的负偏差。我们建议首先考虑占零过多数量的零膨胀模型来拟合零膨胀泊松和二项式计数。从零膨胀的泊松模型估计的泊松参数λ然后用于构建单面c图表,其控制上限基于为λ提供良好覆盖概率的Jeffreys先验区间构造。类似地,从零膨胀二项式模型估计的二项式参数p用于构建单面np图,其控制上限基于二项式比例p的Jeffreys先验区间或Blyth-Still区间构造。还建议使用简单的二选二控制规则来进一步改善这两个建议图表的性能。

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