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The power of monitoring: how to make the most of a contaminated multivariate sample

Cerioli, Andrea, Riani, Marco, Atkinson, Anthony C. and Corbellini, Aldo (2017) The power of monitoring: how to make the most of a contaminated multivariate sample. Statistical Methods and Applications. ISSN 1618-2510

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Identification Number: 10.1007/s10260-017-0409-8

Abstract

Diagnostic tools must rely on robust high-breakdown methodologies to avoid distortion in the presence of contamination by outliers. However, a disadvantage of having a single, even if robust, summary of the data is that important choices concerning parameters of the robust method, such as breakdown point, have to be made prior to the analysis. The effect of such choices may be difficult to evaluate. We argue that an effective solution is to look at several pictures, and possibly to a whole movie, of the available data. This can be achieved by monitoring, over a range of parameter values, the results computed through the robust methodology of choice. We show the information gain that monitoring provides in the study of complex data structures through the analysis of multivariate datasets using different high-breakdown techniques. Our findings support the claim that the principle of monitoring is very flexible and that it can lead to robust estimators that are as efficient as possible. We also address through simulation some of the tricky inferential issues that arise from monitoring.

Item Type: Article
Official URL: https://link.springer.com/journal/10260
Additional Information: © 2017 Springer-Verlag GmbH Germany, part of Springer Nature
Divisions: Statistics
Subjects: Q Science > QA Mathematics
Sets: Departments > Statistics
Date Deposited: 12 Mar 2018 16:10
Last Modified: 20 Apr 2019 01:51
URI: http://eprints.lse.ac.uk/id/eprint/87161

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