Estimating the number of true null hypotheses in multiple hypothesis testing

DOI10.1007/s11222-013-9377-5zbMath1325.62143OpenAlexW1995965715MaRDI QIDQ892486

Hsun-Chih Kuo, Yi-Ting Hwang, Meng Feng Lee, Chun-Chao Wang

Publication date: 19 November 2015

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-013-9377-5

zbMATH Keywords

sensitivitymultiple hypothesis testingfalse discovery rateadaptive FDR controlling procedurenumber of true null hypotheses

Mathematics Subject Classification ID

Paired and multiple comparisons; multiple testing (62J15)

Related Items (4)

Estimating the number of equal components for two high-dimensional mean vectors ⋮ Estimating the proportion of true null hypotheses under dependency: a marginal bootstrap approach ⋮ Two new estimators for the proportion of true null hypotheses in multiple test ⋮ Dependence aliasing and the control of family-wise error rate in multiple hypothesis testing

Uses Software

• R

Cites Work

• Unnamed Item
• Unnamed Item
• Estimating the proportion of false null hypotheses among a large number of independently tested hypotheses
• Evaluations of FDR-controlling procedures in multiple hypothesis testing
• An adaptive step-down procedure with proven FDR control under independence
• Resampling-based multiple testing for microarray data analysis (With comments)
• A step-down multiple hypotheses testing procedure that controls the false discovery rate under independence.
• Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics.
• The control of the false discovery rate in multiple testing under dependency.
• A stochastic process approach to false discovery control.
• Comparisons of estimators of the number of true null hypotheses and adaptive FDR procedures in multiplicity testing
• False Discovery Control for Multiple Tests of Association Under General Dependence
• Adaptive linear step-up procedures that control the false discovery rate
• A sharper Bonferroni procedure for multiple tests of significance
• Comparison of Methods for Estimating the Number of True Null Hypotheses in Multiplicity Testing
• Strong Control, Conservative Point Estimation and Simultaneous Conservative Consistency of False Discovery Rates: A Unified Approach
• A Direct Approach to False Discovery Rates
• Incorporating the number of true null hypotheses to improve power in multiple testing: application to gene microarray data
• Estimating the Proportion of True Null Hypotheses, with application to DNA Microarray Data

This page was built for publication: Estimating the number of true null hypotheses in multiple hypothesis testing