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Optimal lot disposition from Poisson-Lindley count data

机译：泊松林德利计数数据的最佳批次

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摘要

Optimal sampling plans based on overdispersed defect counts for screening lots of outgoing and incoming goods are derived by minimizing the required sample size. Best inspection schemes provide appropriate protections to customers and manufacturers. The stochastic distribution of the number of defects per sampled unit is described by Poisson-Lindley models. Optimal frequentist and Bayesian decision rules for lot disposition are found by solving mixed integer nonlinear programming problems through simulation. The suggested criteria are based on likelihood and posterior odds ratios. The asymptotic normality of the quality score statistic is used to deduce explicit and reasonably accurate approximations of the optimal acceptance sampling plans. The Bayesian approach allows the practitioners to reduce the needed sample size for sentencing lots of high-quality products. For illustrative purposes, the proposed methods are applied to the manufacturing of copper wire. (C) 2019 Elsevier Inc. All rights reserved.
机译：通过最小化所需的样本大小来导出基于过度分散的缺陷计数的基于过度分散的缺陷计数来筛选许多传出和传入商品的计划。最佳检验计划为客户和制造商提供适当的保护。 Poisson-Lindley Models描述了每个采样装置的缺陷数量的随机分布。通过仿真解决混合整数非线性规划问题，发现了最佳频率和贝叶斯决策规则。建议的标准基于可能性和后差比率比率。质量评分统计的渐近正常性用于推导出明确的和合理准确的最佳验收抽样计划的近似。贝叶斯的方法允许从业者减少所需的样本量，以判处大量的高质量产品。出于说明性目的，所提出的方法应用于铜线的制造。 （c）2019 Elsevier Inc.保留所有权利。

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