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 0143-9782Full text not available from this repository.
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.
|Keywords:||Additive outliers • extreme order statistics • standardized spacings|
|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|
|Publisher:||Blackwell Publishing Ltd|
Actions (login required)