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Abstract <p>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.</p>

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Rate of Convergence of Risk Estimate to the Normal Law When Using an FDR-Threshold under Conditions of Weak Dependence

  • 期刊论文

  • 发表日期：

    2025年9月3日

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  • M. O. Vorontsov

摘要

Abstract

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.