Robust detection of a weak signal with redescending M ‐estimators: A comparative study

On finite samples redescending M ‐estimators outperform linear bounded Huber's M ‐estimators. To provide stable detection of a weak signal of arbitrary shape, robust Neyman–Pearson detection rules based on redescending M ‐estimators of location are introduced and studied. It is shown that, on the whole, robust detectors based on redescending M ‐estimators outperform conventional Huber's linear bounded detectors rules under light‐ and heavy‐tailed noise distributions both on large and small samples. Copyright © 2009 John Wiley & Sons, Ltd.