Informatics and Applications
2017, Volume 11, Issue 2, pp 122125
UNIVERSAL THRESHOLDING IN THE MODELS WITH NONGAUSSIAN 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 meansquare risk is close to the minimum.
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[+] About this article
Title
UNIVERSAL THRESHOLDING IN THE MODELS WITH NONGAUSSIAN NOISE
Journal
Informatics and Applications
2017, Volume 11, Issue 2, pp 122125
Cover Date
20170630
DOI
10.14357/19922264170214
Print ISSN
19922264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
thresholding; nonGaussian noise; meansquare risk
Authors
O.V. Shestakov ,
Author Affiliations
Department of Mathematical Statistics, Faculty of Computational Mathematics and Cybernetics, M.V. Lomonosov Moscow State University, 152 Leninskiye Gory, GSP1, Moscow 119991, Russian Federation
Institute of Informatics Problems, Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 442 Vavilov Str., Moscow 119333, Russian Federation
