Efficient robust methods via monitoring for clustering and multivariate data analysis

Riani, Marco, Atkinson, Anthony C., Cerioli, Andrea and Corbellini, Aldo (2019) Efficient robust methods via monitoring for clustering and multivariate data analysis. Pattern Recognition, 88. pp. 246-260. ISSN 0031-3203

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Abstract

Monitoring the properties of single sample robust analyses of multivariate data as a function of breakdown point or efficiency leads to the adaptive choice of the best values of these parameters, eliminating arbitrary decisions about their values and so increasing the quality of estimators. Monitoring the trimming proportion in robust cluster analysis likewise leads to improved estimators. We illustrate these procedures on a sample of 424 cows with bovine phlegmon. For clustering we use a method which includes constraints on the eigenvalues of the dispersion matrices, so avoiding thread shaped clusters. The “car-bike” plot reveals the stability of clustering as the trimming level changes. The pattern of clusters and outliers alters appreciably for low levels of trimming.

Item Type:	Article
Official URL:	https://www.journals.elsevier.com/pattern-recognit...
Additional Information:	© 2018 Elsevier Ltd.
Divisions:	Statistics
Subjects:	Q Science > QA Mathematics
Date Deposited:	18 Dec 2018 16:12
Last Modified:	26 Mar 2024 22:39
URI:	http://eprints.lse.ac.uk/id/eprint/91327

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