Year: 2017
Type: article
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. WIREs Data Mining Knowl Discov 2018, 8:e1236. doi: 10.1002/widm.1236 This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Technologies > Classification Technologies > Structure Discovery and Clustering Technologies > Visualization
DOI: 10.1002/widm.1236
Authors: Peter J. Rousseeuw, Mia Hubert
Institution: KU Leuven
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,480
OpenAlex ID: W2740802195