Rate of Convergence of Risk Estimate to the Normal Law When Using an FDR-Threshold under Conditions of Weak Dependence
This paper considers an approach to solving the problem of noise removal in a large array of sparse data under conditions of weak dependence based on controlling the false discovery rate. An order estimate is obtained for the rate of convergence of the root-mean-square (RMS) risk estimate of this approach to the normal law.
Authors
- M. O. Vorontsov
Institutions
- Lomonosov Moscow State University (RU)
- Moscow Center For Continuous Mathematical Education (RU)
Publication Details
- Journal
- Moscow University Computational Mathematics and Cybernetics
- Published
- 2025-09-01
- DOI
- https://doi.org/10.3103/s0278641925700116
- Primary Topic
- Statistical Methods and Inference
- Type
- article
- Field-Weighted Citation Impact
- 0.00