RMS Risk Analysis when Using Multiple Hypothesis Testing Select Parameters of Thresholding under Conditions of Weak Dependence

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发表日期：

2024年6月11日

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M. O. Vorontsov

摘要

Abstract

This work considers an approach to solving the problem of noise removal in a large dataset from sparsity class \(m_{p}\) under conditions of weak dependence based on controlling the false discovery rate. An upper asymptotic bound for the RMS risk is obtained.