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Applying the
Q
n
Estimator Online
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| Studies in Classification, Data Analysis, and Knowledge Organization |
Data Analysis, Machine Learning and Applications
Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg,
March 7–9, 2007
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| 10.1007/978-3-540-78246-9_33 |
| Christine Preisach, Hans Burkhardt, Lars Schmidt-Thieme and Reinhold Decker |
Applying the Qn Estimator Online
Robin Nunkesser5  , Karen Schettlinger6 and Roland Fried6
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Department of Computer Science, Univ. Dortmund, 44221 Dortmund, Germany |
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Department of Statistics, Univ. Dortmund, 44221 Dortmund, Germany |
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 Qn estimator and show by simulations that it leads to more powerful tests than other highly robust scale estimators.
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