Anomaly Detection by Robust Statistics

Abstract

Real data often contain anomalous cases, also known as outliers. These may spoil the resulting analysis but they may also contain valuable information. In either case, the ability to detect such anomalies is essential. A useful tool for this purpose is robust statistics, which aims to detect the outliers by first fitting the majority of the data and then flagging data points that deviate from it. We present an overview of several robust methods and the resulting graphical outlier detection tools. We discuss robust procedures for univariate, low-dimensional, and high-dimensional data, such as estimating location and scatter, linear regression, principal component analysis, classification, clustering, and functional data analysis. Also the challenging new topic of cellwise outliers is introduced.

To appear in WIREs Data Mining and Knowledge Discovery

References in corpus (4)

• High-Breakdown Robust Multivariate Methods
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• Propagation of outliers in multivariate data
• Multivariate and functional classification using depth and distance

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