Premium
Robust detection of a weak signal with redescending M ‐estimators: A comparative study
Author(s) -
Shevlyakov Georgy,
Lee Jae Won,
Lee Kyung Min,
Shin Vladimir,
Kim Kiseon
Publication year - 2010
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.1104
Subject(s) - estimator , bounded function , detector , m estimator , mathematics , signal (programming language) , robust statistics , detection theory , statistics , computer science , algorithm , mathematical analysis , telecommunications , programming language
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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom