Robust statistics for outlier detection
Robust statistics for outlier detection
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DOI：
10.1002/widm.2
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发表时间：
2011-01-01
影响因子：
7.8
通讯作者：
Hubert, Mia
中科院分区：
文献类型：
--
作者：
Rousseeuw, Peter J.;Hubert, Mia
在分析数据时，离群观测值会导致问题，因为它们可能会强烈影响结果。稳健统计的目的是通过搜索大多数数据拟合的模型来检测离群值。我们提出了几个强大的方法和离群值检测工具的概述。我们讨论了单变量，低维和高维数据，如估计的位置和分散，线性回归，主成分分析和分类的强大程序。(C)John Wiley & Sons，Inc. WIREs数据挖掘知识发现2011 1 73-79 DOI：10.1002/widm.2
When analyzing data, outlying observations cause problems because they may strongly influence the result. Robust statistics aims at detecting the outliers by searching for the model fitted by the majority of the data. We present an overview of several robust methods and outlier detection tools. We discuss robust procedures for univariate, low-dimensional, and high-dimensional data such as estimation of location and scatter, linear regression, principal component analysis, and classification. (C) 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 73-79 DOI: 10.1002/widm.2