PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Chen J. Y., Kuruoglu E. E., So H. C. Optimum linear regression in additive Cauchy-Gaussian noise. In: Signal Processing, vol. 106 pp. 312 - 318. Elsevier, 2015.
 
 
Abstract
(English)
We study the estimation problem of linear regression in the presence of a new impulsive noise model, which is a sum of Cauchy and Gaussian random variables in time domain. The probability density function (PDF) of this mixture noise, referred to as the Voigt profile, is derived from the convolution of the Cauchy and Gaussian PDFs. To determine the linear regression parameters, the maximum likelihood estimator (MLE) is developed first. Since the Voigt profile suffers from a complicated analytical form, an M-estimator with the pseudo-Voigt function is also derived. In our algorithm development, both scenarios of known and unknown density parameters are considered. For the latter case, we estimate the density parameters by utilizing the empirical characteristic function prior to applying the MLE. Simulation results show that the performance of both proposed methods can attain the Cramér-Rao lower bound.
URL: http://www.sciencedirect.com/science/article/pii/S0165168414003624
DOI: 10.1016/j.sigpro.2014.07.028
Subject Impulsive noise
Cauchy-Gauss mixture
Voigt profile
G.3 PROBABILITY AND STATISTICS Distribution functions
G.3 PROBABILITY AND STATISTICS Probabilistic algorithms (including Monte Carlo)
62F15 Bayesian inference
62M10 Time series, auto-correlation, regression, etc.


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