Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency.
Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency.
The spots were selected on the basis of uncorrected p < 0.05 so to avoid the possible exclusion of factors that could contribute to the model even if their change should not be considered as significant in multiple testing.
In multiple testing problems where informative covariates can be naturally assigned to hypotheses, e.g. hypotheses are arranged in spacetime, we propose and validate a likelihood based method that provides more powerful inference while retaining control over false discovery rate.
We define the type I and type II errors of a test in this design and show how a prescribed type I error can be maintained by using the closed testing principle in multiple testing.
Testing models in multiple testing regions will lead to quicker assessments of the model performance.
To control the false discovery rate in multiple testing (i.e. reduce the probability of type I errors), the Benjamini-Hochberg method was used.
Univariate analysis of metabolomics data usually includes hundreds of tests (one for each metabolite), therefore the control of false discovery rate (FDR) in multiple testing is very important.
The memories of pulling countless all-nighters to work on essays, sitting four and a half hours in multiple testing centers, and begging students to participate in club meetings seem to hit me all at once.
