Detecting Outliers in Multivariate Laboratory Data
Detecting Outliers in Multivariate Laboratory Data
复制标题
DOI：
10.1080/10543400802369046
复制
发表时间：
2008-01-01
影响因子：
1.1
通讯作者：
Southworth, Harry
中科院分区：
文献类型：
--
作者：
Southworth, Harry
在临床试验中收集的实验室数据通常包括离群值，这些通常是最感兴趣的观察结果。在高维环境中，离群值可能难以检测，并且可以通过经典统计方法来掩盖。描述了一种以暴露异常值的方式绘制鲁棒缩放数据的方法，并给出了一个应用。
Laboratory data collected in clinical trials often include outliers, and these are often the observations of most interest. In high dimensional settings, outliers can be difficult to detect and can be masked by classical statistical methods. A method of plotting robustly scaled data in such a way as to expose outliers is described and an application is presented.