Rate of convergence of the risk estimator to the normal law when using the FDR threshold under weak dependence

Recieved: 05/12/2025

Accepted: 05/26/2025

Keywords: threshold processing, multiple hypothesis testing, mean–square risk estimator

To cite this article

Vorontsov M. O. Rate of convergence of the risk estimator to the normal law when using the FDR threshold under weak dependence. // Moscow University Journal. Series 15. Computational Mathematics and Cybernetics. 2025. N 3, p.23-31 https://doi.org/10.55959/MSU/0137–0782–15–2025–49–3–23–31.

This work is licensed under a Сreative Commons Atribiution - NonCommercial 4.0 International (CC BY-NC 4.0)

Vorontsov M. O. (Lomonosov Moscow State University (MSU))

Abstract

The paper considers an approach to solving the problem of noise removal in a large array of sparse data under weak dependence. The approach is based on the method of controlling the false discovery rate (FDR). For this approach, the rate of convergence of the mean–square risk estimator to the normal law is obtained.