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In signal processing, it is typical to develop or use a method based on a given model. In practice, however, the authors almost never know the actual model and they hope that the assumed model is in the neighborhood of the true one. If deviations exist, the method may be more or less sensitive to them. Therefore, it is important to know more about this sensitivity, or in other words, how robust the method is to model deviations. To that end, it is useful to have a metric that can quantify the robustness of the method. In this paper, they propose a procedure for developing a variety of metrics for measuring robustness.
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