Incorporating the number of true null hypotheses to improve power in multiple testing: application to gene microarray data

DOI10.1080/10629360600648651zbMath1127.62113OpenAlexW2169611148MaRDI QIDQ5438710

Huey-Miin Hsueh, James J. Chen, Chen-An Tsai

Publication date: 28 January 2008

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10629360600648651

zbMATH Keywords

\(p\)-valuesfalse discovery rate (FDR)number of true null hypothesescomplete null hypothesisfamily-wise error rate (FWE)

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Medical applications (general) (92C50) Paired and multiple comparisons; multiple testing (62J15)

Related Items (3)

Estimating the number of true null hypotheses in multiple hypothesis testing ⋮ Evaluations of FDR-controlling procedures in multiple hypothesis testing ⋮ Comparisons of estimators of the number of true null hypotheses and adaptive FDR procedures in multiplicity testing

Cites Work

• Unnamed Item
• Multiple hypothesis testing in microarray experiments.
• A mixture model approach for the analysis of microarray gene expression data.
• The control of the false discovery rate in multiple testing under dependency.
• Comparison of Methods for Estimating the Number of True Null Hypotheses in Multiplicity Testing
• Rectangular Confidence Regions for the Means of Multivariate Normal Distributions

This page was built for publication: Incorporating the number of true null hypotheses to improve power in multiple testing: application to gene microarray data