The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Robust statistics for outlier detection
Abstract
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. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 73‐79 DOI: 10.1002/widm.2 This article is categorized under: Algorithmic Development > Biological Data Mining Algorithmic Development > Spatial and Temporal Data Mining Application Areas > Health Care Technologies > Structure Discovery and Clustering
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Cites
Finding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344
Self-citationRobust estimation of a location parameter
Huber, P. J. (1964) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177703732 · Cited by 7,228
Huber, P. J. (1981) · Wiley Series in Probability and Statistics · doi:10.1002/0471725250 · Cited by 7,066
Cited by
Progress in Outlier Detection Techniques: A Survey
Wang, H., Bah, M. J., & Hammad, M. (2019) · IEEE Access · doi:10.1109/access.2019.2932769 · Cited by 523
Castellazzi, G., Cuzzoni, M. G., Cotta Ramusino, M., Martinelli, D., Denaro, F., Ricciardi, A., Vitali, P., Anzalone, N., Bernini, S., Palesi, F., et al. (2020) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2020.00025 · Cited by 135
Univariate outliers: A conceptual overview for the nurse researcher
Mowbray, F. I., Fox-Wasylyshyn, S. M., & El-Masri, M. M. (2019) · Canadian Journal of Nursing Research · doi:10.1177/0844562118786647 · Cited by 117
Baier, C., Göttsche, M., Hellmann, A., & Schiemann, F. (2022) · Journal of Business Ethics · doi:10.1007/s10551-020-04719-7 · Cited by 61
Lyu, F., Wang, S., Han, S. Y., Catlett, C., & Wang, S. (2022) · Urban Informatics · doi:10.1007/s44212-022-00002-4 · Cited by 55
Metadata
- Authors
- Rousseeuw, P. J., & Hubert, M.
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2011-01
- Volume
- 1
- Issue
- 1
- Pages
- 73-79
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W2048430744
- Cited by
- 792
- References
- 3
- FWCI
- 10.85
- Type
- journal article
- Language
- en
- Open access
- closed
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