Informatics and Applications
2018, Volume 12, Issue 3, pp 14-17
MEAN-SQUARE THRESHOLDING RISK WITH A RANDOM SAMPLE SIZE
Abstract
Nonlinear methods of signal de-noising based on the threshold processing of wavelet coefficients are widely used in various application areas. These methods have gained their popularity due to the speed of the algorithms for constructing estimates and the possibility of adapting to functions belonging to different classes of regularity better than linear methods. When applying thresholding techniques, it is usually assumed that the number of wavelet coefficients is fixed and the noise distribution is Gaussian. This model has been well studied in the literature, and optimal threshold values have been calculated for different classes of signals. However, in some situations, the sample size is not known in advance and is modeled by a random variable. The present author considers a model with a random number of observations containing Gaussian noise and estimates the order of the mean-square risk with increasing sample size.
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[+] About this article
Title
MEAN-SQUARE THRESHOLDING RISK WITH A RANDOM SAMPLE SIZE
Journal
Informatics and Applications
2018, Volume 12, Issue 3, pp 14-17
Cover Date
2018-08-30
DOI
10.14357/19922264180302
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
thresholding; random sample size; 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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