Transformation-Based Robust Semiparametric Estimation

We address the problem of parameter estimation of signals in noise of unknown distribution and propose a semiparametric estimator. Classical parametric estimators, such as the least-squares or Huber's minimax methods, are limited in terms of robustness and generally suboptimal in practice. Alte...

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Bibliographic Details
Published in:IEEE signal processing letters 2008, Vol.15, p.845-848
Main Authors: Hammes, U., Wolsztynski, E., Zoubir, A.M.
Format: Article
Language:English
Subjects:
Amplitude estimation
Electromagnetic interference
Impulsive noise
Kernel
Maximum likelihood estimation
Minimax techniques
Noise level
Noise robustness
Parameter estimation
Probability density function
robust estimation
semiparametric estimation
sinusoids amplitude estimation
transformation density estimation
Vectors
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Summary:We address the problem of parameter estimation of signals in noise of unknown distribution and propose a semiparametric estimator. Classical parametric estimators, such as the least-squares or Huber's minimax methods, are limited in terms of robustness and generally suboptimal in practice. Alternative methods which are based on nonparametric probability density function (pdf) estimation have been proposed recently. They automatically adapt to the measurements and thus outperform classical techniques. The semiparametric technique we suggest, which also automatically adapts to the data and relies on transformation pdf estimation, provides a further improvement and overcomes the computational weaknesses of the previous methods. The power of the technique is highlighted in an example of amplitude estimation of sinusoidal signals in impulsive noise.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2008.2002701