Robust Statistical Analysis of Interlaboratory Studies
针对实验室间研究中常见的异常值和长尾分布问题，提出一种稳健的统计分析方法，并通过理论推导和蒙特卡洛模拟验证其有效性，适用于分析测试方法中的重复误差和实验室偏差。
A common procedure in testing analytical methods is to send a portion of each of a number of samples to each of several laboratories. The results of such a study are submitted to statistical analysis to determine the two important variance components in the problem: replication error and laboratory bias. Outliers are relatively common in these data both among laboratory effects and among the residuals. This paper presents a method of analysis for interlaboratory studies that is robust to the existence of outliers and long-tailed distributions of random effects. Theoretical considerations as well as a Monte Carlo study are adduced as support for this new technique.