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Two-sided variable inspection plans for arbitrary continuous populations with unknown distribution

机译：双面可变检验计划，随机分发的任意连续群体

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摘要

The ordinary variable inspection plans rely on the normality of the underlying populations. However, this assumption is vague or even not satisfied. Moreover, ordinary variable sampling plans are sensitive against deviations from the distribution assumption. Nonconforming items occur in the tails of the distribution. They can be approximated by a generalized Pareto distribution (GPD). We investigate several estimates of their parameters according to their usefulness not only for the GPD, but also for arbitrary continuous distributions. The likelihood moment estimates (LMEs) of Zhang (Aust N Z J Stat 49:69-77, 2007) and the Bayesian estimate (ZSE) of Zhang and Stephens (Technometrics 51:316-325, 2009) turn out to be the best for our purpose. Then, we use these parameter estimates to estimate the fraction defective. The asymptotic normality of the LME (cf. Zhang 2007) and that of the fraction defective are used to construct the sampling plan. The difference to the sampling plans constructed in Kossler (Allg Stat Arch 83:416-433, 1999; in: Steland, Rafajlowicz, Szajowski (eds) Stochastic models, statistics, and their applications, Springer, Heidelberg, pp 93-100, 2015) is that we now use the new parameter estimates. Moreover, in contrast to the aforementioned papers, we now also consider two-sided specification limits. An industrial example illustrates the method.
机译：普通的可变检查计划依赖于潜在人群的正常性。然而，这种假设模糊甚至不满意。此外，普通的变量抽样计划对与分布假设的偏差敏感。不合格的物品发生在分布的尾部。它们可以通过广义帕肌架分布（GPD）来近似。我们根据GPD的实用性调查了几个参数估计，也可以针对任意连续分布进行调查。张（AUST NZJ STAT 49：69-77,2007）和张和斯蒂芬斯（Techmetrics 51：316-325,2009）的呼应时刻（LMES）和贝叶斯估计（ZSE）结果是最好的目的。然后，我们使用这些参数估计来估计部分有缺陷。使用LME（CF. Zhang 2007）的渐近常态以及部分有缺陷的缺陷来构建取样计划。在Kossler（allg Stat Arch 83：416-433,1999中构建的抽样计划的差异;在：Steland，Rafajlowicz，Szajowski（EDS）随机模型，统计数据及其应用，Springer，Heidelberg，2015年第93-100页，PP 93-100 ）我们现在使用新的参数估计。此外，与上述文件相比，我们现在还考虑了双面规范限制。工业例子说明了该方法。

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