基于自助法的测量精密度估计与推断（ISO 5725）
Bootstrap-based estimation and inference for measurement precision under ISO 5725
AI总结：
本文提出基于自助法的精密度估计与推断方法，针对ISO 5725数据设计重采样策略，改进偏置校正以获得更可靠的置信区间，适用于实验室间精密度分析。
摘要：
ISO 5725系列通过重复性、实验室间和再现性方差来界定实验室间精密度，但在此单因素随机效应设置中应用自助法的实用指导仍然有限。我们研究了针对ISO 5725数据定制的重采样策略，并扩展了偏置校正思想，以获得简单的调整点估计器和方差分量的置信区间。通过大量模拟，这些模拟反映了现实的研究规模和方差比，我们评估了准确性、稳定性及覆盖性，并将重采样程序与方差分析（ANOVA）估计器和常见近似区间进行了对比。结果表明，调整后的实验室内重采样在小到中等设计中提供准确且稳定的点估计，而两阶段策略——先重采样实验室再在每个实验室内重采样——配以偏置校正和加速区间，提供了最可靠的（近名义或保守）置信区间。在极端设计下，如非常小的样本或主导的实验室间变异时，性能会下降，这明确了何时需要额外谨慎。一个来自ISO 5725-4数据集的案例研究展示了推荐程序在实践中的表现以及与方差分析和近似方法的比较。我们最后提供具体的指导，说明如何在实验室间研究中实施基于重采样的精密度分析：使用调整后的实验室内重采样进行点估计，采用两阶段策略配以偏置校正和加速区间进行区间估计。
英文摘要：
The ISO 5725 series frames interlaboratory precision through repeatability, between-laboratory, and reproducibility variances, yet practical guidance on deploying bootstrap methods within this one-way random-effects setting remains limited. We study resampling strategies tailored to ISO 5725 data and extend a bias-correction idea to obtain simple adjusted point estimators and confidence intervals for the variance components. Using extensive simulations that mirror realistic study sizes and variance ratios, we evaluate accuracy, stability, and coverage, and we contrast the resampling-based procedures with ANOVA-based estimators and common approximate intervals. The results yield a clear division of labor: adjusted within-laboratory resampling provides accurate and stable point estimation in small-to-moderate designs, whereas a two-stage strategy-resampling laboratories and then resampling within each-paired with bias-corrected and accelerated intervals offers the most reliable (near-nominal or conservative) confidence intervals. Performance degrades under extreme designs, such as very small samples or dominant between-laboratory variation, clarifying when additional caution is warranted. A case study from an ISO 5725-4 dataset illustrates how the recommended procedures behave in practice and how they compare with ANOVA and approximate methods. We conclude with concrete guidance for implementing resampling-based precision analysis in interlaboratory studies: use adjusted within-laboratory resampling for point estimation, and adopt the two-stage strategy with bias-corrected and accelerated intervals for interval estimation.
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