Abstract
Reliable automatic methods are needed for statistical online monitoring of noisy time series. Application of a robust scale estimator allows to use adaptive thresholds for the detection of outliers and level shifts. We propose a fast update algorithm for the Q n estimator and show by simulations that it leads to more powerful tests than other highly robust scale estimators.
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Nunkesser, R., Schettlinger, K., Fried, R. (2008). Applying the Q n Estimator Online. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_33
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DOI: https://doi.org/10.1007/978-3-540-78246-9_33
Publisher Name: Springer, Berlin, Heidelberg
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