On the robust detection of edges in time series filtering

Abrupt shifts in the level of a time series represent important information and should be preserved in statistical signal extraction. We investigate rules for detecting level shifts that are resistant to outliers and which work with only a short time delay. The properties of robustified versions of the t-test for two independent samples and its non-parametric alternatives are elaborated under different types of noise. Trimmed t-tests, median comparisons, robustified rank and ANOVA tests based on robust scale estimators are compared.