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使用格拉布斯检验和科克伦检验来识别异常值。

Using the Grubbs and Cochran tests to identify outliers.

出版信息

Anal Methods. 2015 Sep 24;7(19):7948-7950. doi: 10.1039/c5ay90053k.

Abstract

In a previous Technical Brief (TB No. 39) three approaches for tackling suspect results were summarised. Median-based and robust methods respectively ignore and down-weight measurements at the extremes of a data set, while significance tests can be used to decide if suspect measurements can be rejected as outliers. This last approach is perhaps still the most popular one, and is used in several standards, despite possible drawbacks. Here significance testing for identifying outliers is considered in more detail with the aid of some typical examples.

摘要

在之前的一份技术简报(第39号)中,总结了三种处理可疑结果的方法。基于中位数的方法和稳健方法分别忽略数据集两端的测量值并降低其权重,而显著性检验可用于判定可疑测量值是否可作为异常值剔除。尽管存在一些潜在缺点,但最后这种方法可能仍是最常用的,并且在多个标准中都有应用。在此,借助一些典型示例更详细地探讨用于识别异常值的显著性检验。

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