Control charts for the mean based on robust two-sample tests
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Date
2016
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Abstract
We propose and investigate robust control charts for the detection of sudden shifts in sequences of very noisy observations with a naturally slowly varying mean. They sequentially
apply local two-sample tests for the location problem. Thus, no previous knowledge about
the in-control behaviour is necessary.
We identify critical values for the tests to achieve a desired in-control average run length
(ARL_0) with extensive simulations. Control charts based on nonparametric tests or a randomization principle provide a satisfactory run length behaviour for different error distributions. They possess a nearly distribution-free ARL_0 and are fast in detecting present signal jumps in a time series.
In our simulations and exemplary real-world applications from biosignal analysis, a test based
on the two-sample Hodges-Lehmann estimator leads to very promising results regarding distribution independence, robustness and detection speed.
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Keywords
biosignal analysis, monitoring, two-sample tests, time series, robust control charts, change-point detection