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. 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.