1.1 本实践提供了一个逐步的程序，用于应用广义极端学习偏差（GESD）多离群值程序来同时识别数据集中的多个离群值。（见参考书目。） 1.2 此实践适用于包含在连续数字尺度上表示的观察值的数据集。 1.3 本规程适用于包含至少六个观察值的数据集。 1.4 这种做法适用于正态（高斯）模型合理地适合于数据集中观察值的分布表示的数据集。 1.5 与本实践设置的决策标准相关的异常值的错误识别概率为0。 1 1.6 建议在熟悉与GESD技术相关的统计原则和假设的人员的指导下执行本实践。 1.7 本标准并非旨在解决与其使用相关的所有安全问题（如有）。本标准的用户有责任在使用前制定适当的安全、健康和环境实践，并确定监管限制的适用性。 1.8 本国际标准是根据世界贸易组织技术性贸易壁垒（TBT）委员会发布的《关于制定国际标准、指南和建议的原则的决定》中确立的国际公认标准化原则制定的。 ====意义和用途====== 3.1 GESD程序可用于同时识别多达预定数量的异常值( r )在数据集中，无需预先检查数据集并 先验的 关于潜在异常值的位置和数量的决策。 3.2 GESD程序对掩蔽具有鲁棒性。掩蔽描述了一种现象，即多个异常值的存在可以阻止异常值识别过程将数据集中的任何观察值声明为异常值。 3.3 GESD程序对自动化友好，因此可以很容易地编程为自动计算机算法。

1.1 This practice provides a step by step procedure for the application of the Generalized Extreme Studentized Deviate (GESD) Many-Outlier Procedure to simultaneously identify multiple outliers in a data set. (See Bibliography.) 1.2 This practice is applicable to a data set comprising observations that is represented on a continuous numerical scale. 1.3 This practice is applicable to a data set comprising a minimum of six observations. 1.4 This practice is applicable to a data set where the normal (Gaussian) model is reasonably adequate for the distributional representation of the observations in the data set. 1.5 The probability of false identification of outliers associated with the decision criteria set by this practice is 0.01. 1.6 It is recommended that the execution of this practice be conducted under the guidance of personnel familiar with the statistical principles and assumptions associated with the GESD technique. 1.7 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety, health, and environmental practices and determine the applicability of regulatory limitations prior to use. 1.8 This international standard was developed in accordance with internationally recognized principles on standardization established in the Decision on Principles for the Development of International Standards, Guides and Recommendations issued by the World Trade Organization Technical Barriers to Trade (TBT) Committee. ====== Significance And Use ====== 3.1 The GESD procedure can be used to simultaneously identify up to a pre-determined number of outliers ( r ) in a data set, without having to pre-examine the data set and make a priori decisions as to the location and number of potential outliers. 3.2 The GESD procedure is robust to masking. Masking describes the phenomenon where the existence of multiple outliers can prevent an outlier identification procedure from declaring any of the observations in a data set to be outliers. 3.3 The GESD procedure is automation-friendly, and hence can easily be programmed as automated computer algorithms.