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Istituto di Scienza e Tecnologie dell'Informazione     
Chen Y., Kuruoglu E. E., So H. C., Huang L., Wang W. Density parameter estimation for additive Cauchy-Gaussian mixture. In: SSP 2014 - IEEE Workshop on Statistical Signal Processing (Gold Coast, VIC, Australia, 29 June - 2 July 2014). Proceedings, pp. 197 - 200. IEEE, 2014.
 
 
Abstract
(English)
In this paper, a mixture noise model, which is a sum of symmetric Cauchy and zero-mean Gaussian random variables in time domain, is studied. The Cauchy and Gaussian distributions are characterized by the unknown median and variance, respectively. The probability density function (PDF) and characteristic function (CF) of the mixture are also investigated which are calculated by the convolution of the two PDFs, and product of the two CFs, respectively. Due to the complication of the resultant PDF, typical approaches such as maximum likelihood estimator may not be able to estimate parameters reliably. Based on the resultant CF, we propose to employ the fractional lower-order moment estimator for their computation. Simulation results show the mean square error performance of the proposed method and a comparison with the Cramer-Rao lower bound is also provided.
URL: http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6884609
DOI: 10.1109/SSP.2014.6884609
Subject Additive Cauchy-Gaussian noise
Mixture noise
Cauchy distribution
Voigt function
Fractional lower-order moment
G.3 PROBABILITY AND STATISTICS Distribution functions
I.2.6 Learning - Parameter Learning
G.3 PROBABILITY AND STATISTICS Time series analysis
62G07 Density estimation
97K60 Distributions and stochastic processes
60E07 Infinitely divisible distributions; stable distributions


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