Rate of convergence of a risk estimator to the normal law in a multiple hypothesis testing problem using the FDR threshold

DOI10.3103/S0278641921030043zbMath1479.62058OpenAlexW3201721396MaRDI QIDQ2058711

S. I. Palionnaya

Publication date: 9 December 2021

Published in: Moscow University Computational Mathematics and Cybernetics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3103/s0278641921030043

zbMATH Keywords

multiple hypothesis testingrisk estimatorfalse discovery rate (FDR) thresholdrate of convergence to normal law

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Hypothesis testing in multivariate analysis (62H15) Paired and multiple comparisons; multiple testing (62J15)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• On false discovery rate thresholding for classification under sparsity
• Asymptotic minimaxity of false discovery rate thresholding for sparse exponential data
• Adapting to unknown sparsity by controlling the false discovery rate
• Strong consistency of the risk estimator in multiple hypothesis testing with the FDR threshold
• A Direct Approach to False Discovery Rates

This page was built for publication: Rate of convergence of a risk estimator to the normal law in a multiple hypothesis testing problem using the FDR threshold