Informatics and Applications
2017, Volume 11, Issue 2, pp 122-125
UNIVERSAL THRESHOLDING IN THE MODELS WITH NON-GAUSSIAN NOISE
Abstract
A common assumption in nonparametric signal estimation is that the signal function belongs to a certain
class. For example, it may be piecewise continuous or piecewise differentiable and have a compact support. These
assumptions, as a rule, make it possible to economically represent a signal function in a specially selected basis in
such a way that the useful signal is concentrated in a relatively small number of large expansion coefficients. Then,
threshold processing removes noisy coefficients. Typically, the noise distribution is assumed to be Gaussian. This
model has been well studied in the literature and optimal thresholding parameters have been calculated for different
classes of signal functions. The paper considers the problem of constructing an estimate for the signal function from
the observations containing additive noise, whose distribution belongs to quite a wide class. The authors calculate
the values of universal thresholding parameters for which the mean-square risk is close to the minimum.
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[+] About this article
Title
UNIVERSAL THRESHOLDING IN THE MODELS WITH NON-GAUSSIAN NOISE
Journal
Informatics and Applications
2017, Volume 11, Issue 2, pp 122-125
Cover Date
2017-06-30
DOI
10.14357/19922264170214
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
thresholding; non-Gaussian noise; mean-square risk
Authors
O.V. Shestakov  , 
Author Affiliations
Department of Mathematical Statistics, Faculty of Computational Mathematics and Cybernetics, M.V. Lomonosov Moscow State University, 1-52 Leninskiye Gory, GSP-1, Moscow 119991, Russian Federation
Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
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