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Cited by 406

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

Robust Estimates of Location and Dispersion for High-Dimensional Datasets

Maronna, Ricardo A, Zamar, Ruben H·2002·Technometrics

Abstract

The computing times of high-breakdown point estimates of multivariate location and scatter increase rapidly with the number of variables, which makes them impractical for high-dimensional datasets, such as those used in data mining. We propose an estimator of location and scatter based on a modified version of the Gnanadesikan–Kettenring robust covariance estimate. We compare its behavior with that of the Stahel–Donoho (SD) and Rousseeuw and Van Driessen's fast MCD (FMCD) estimates. In simulations with contaminated multivariate normal data, our estimate is almost as good as SD and clearly better than FMCD. It is much faster than both, especially for large dimension. We give examples with real data with dimensions between 5 and 93, in which the proposed estimate is as good as or better than SD and FMCD at detecting outliers and other structures, with much shorter computing times.

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Metadata

Authors
Maronna, R. A., & Zamar, R. H.
Venue
Technometrics
Published
2002-11
Volume
44
Issue
4
Pages
307-317
ISSN
  • 0040-1706 (ISSN-L)
  • 1537-2723
OpenAlex
W2049454545
Cited by
406
References
26
FWCI
5.40
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Robust Estimates of Location and Dispersion for High-Dimensional Datasets. https://app.cochat.ai/papers/10.1198/004017002188618509
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Maronna, R. A., & Zamar, R. H. (2002). Robust Estimates of Location and Dispersion for High-Dimensional Datasets. Technometrics. https://doi.org/10.1198/004017002188618509

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