Cookies?
Library Header Image
LSE Research Online LSE Library Services

Efficient robust methods via monitoring for clustering and multivariate data analysis

Riani, Marco and Atkinson, Anthony C. and 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

[img] Text - Accepted Version
Restricted to Repository staff only until 19 November 2019.

Download (3MB) | Request a copy

Identification Number: 10.1016/j.patcog.2018.11.016

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
Sets: Departments > Statistics
Date Deposited: 18 Dec 2018 16:12
Last Modified: 18 Dec 2018 16:12
URI: http://eprints.lse.ac.uk/id/eprint/91327

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics