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Additive Outlier Detection via Extreme-Value Theory.

Burridge, P. and Taylor, A.M.R. (2006) Additive Outlier Detection via Extreme-Value Theory. Journal of Time Series Analysis, 27 (5). pp. 685-701. ISSN 0143-9782

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This article is concerned with detecting additive outliers using extreme value methods. The test recently proposed for use with possibly non-stationary time series by Perron and Rodriguez [Journal of Time Series Analysis (2003) vol. 24, pp. 193–220], is, as they point out, extremely sensitive to departures from their assumption of Gaussianity, even asymptotically. As an alternative, we investigate the robustness to distributional form of a test based on weighted spacings of the sample order statistics. Difficulties arising from uncertainty about the number of potential outliers are discussed, and a simple algorithm requiring minimal distributional assumptions is proposed and its performance evaluated. The new algorithm has dramatically lower level-inflation in face of departures from Gaussianity than the Perron–Rodriguez test, yet retains good power in the presence of outliers.

Item Type: Article
Keywords: Additive outliers • extreme order statistics • standardized spacings
Institution: The University of York
Academic Units: The University of York > Economics and Related Studies (York)
Depositing User: York RAE Import
Date Deposited: 30 Mar 2009 19:04
Last Modified: 30 Mar 2009 19:04
Published Version: http://dx.doi.org/10.1111/j.1467-9892.2006.00483.x
Status: Published
Publisher: Blackwell Publishing Ltd
Identification Number: 10.1111/j.1467-9892.2006.00483.x
URI: http://eprints.whiterose.ac.uk/id/eprint/6962

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