Informatics and Applications
2023, Volume 17, Issue 2, pp 34-40
MEAN-SQUARE RISK OF THE FDR PROCEDURE UNDER WEAK DEPENDENCE
- M. O. Vorontsov
- O. V. Shestakov
Abstract
In many application areas, the problem of processing large amounts of data arises. In this case, before processing, the data array is often subjected to some transformation leading to a "sparse" or "economical" representation in which the absolute value of most elements of the array is equal to zero (or sufficiently small).
In addition, as a result of interference when receiving and transmitting data, they become corrupted with noise and it is desirable to remove this noise during further processing. The resulting task is mathematically equivalent to some multiple hypothesis testing problems. Previously, to solve this problem under conditions of normality, independence, and sparsity of data, a procedure based on the method of controlling the average proportion of erroneously rejected hypotheses was proposed (False Discovery Rate, FDR). In this paper, the authors study the asymptotics of the mean-square risk of this procedure in the case of a weak dependence in the data.
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[+] About this article
Title
MEAN-SQUARE RISK OF THE FDR PROCEDURE UNDER WEAK DEPENDENCE
Journal
Informatics and Applications
2023, Volume 17, Issue 2, pp 34-40
Cover Date
2023-07-10
DOI
10.14357/19922264230205
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
thresholding; multiple hypothesis testing; mean-square risk
Authors
M. O. Vorontsov  ,  and O. V. Shestakov , , 
Author Affiliations
M. V. Lomonosov Moscow State University, 1-52 Leninskie Gory, GSP-1, Moscow 119991, Russian Federation
Moscow Center for Fundamental and Applied Mathematics, M.V. Lomonosov Moscow State University,
1 Leninskie Gory, GSP-1, Moscow 119991, Russian Federation
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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