Journal of Time Series Analysis

Additive Outlier Detection Via Extreme‐Value Theory

Journal Article

Abstract.  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.

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