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A Control Chart Based on A Nonparametric Multivariate Change-Point Model

机译:基于非参数多元变化点模型的控制图

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Phase-Il statistical process control (SPC) procedures are designed to detect a change in distribution when a possibly never-ending stream of observations is collected. Several techniques have been proposed to detect a shift in location vector when each observation consists of multiple measurements. These procedures require the user to make assumptions about the distribution of the process readings, to assume that process parameters are known, or to collect a large training sample before monitoring the ongoing process for a change in distribution. We propose a nonparametric procedure for multivariate phase-ll statistical process control designed to detect shifts in location vector that relaxes these requirements based on an approximately distribution free multivariate test statistic. This procedure may not be appropriate for some multivariate distributions with unusual dependence structure between vector components. A diagnostic tool that can be used if a historical sample of data is available is provided to assist user in determining if the proposed procedure is appropriate for a given application.
机译:十一期统计过程控制(SPC)程序旨在在收集可能永无止境的观测流时检测分布的变化。已经提出了几种技术来检测每个观测值包括多个测量值时位置矢量的偏移。这些过程要求用户对过程读数的分布进行假设,以假定过程参数已知,或者在监视正在进行的过程中的分布变化之前收集大量的培训样本。我们提出了一种用于多相II期统计过程控制的非参数过程,该过程旨在检测位置矢量的偏移量,从而基于近似无分布的多元检验统计量来放宽这些要求。此过程可能不适用于某些矢量分量之间具有异常依赖性结构的多元分布。如果有历史数据样本可用,则可以使用一种诊断工具,以帮助用户确定建议的过程是否适合给定的应用程序。

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