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This talk proposes the first differentially private procedure for controlling the false discovery rate (FDR) in multiple hypothesis testing.
In some cases, experimenters may have difficulty in applying those step-up procedures in multiple hypothesis testing because of the computational limitation of existing algorithms in evaluating the critical values for a large number of multiple comparisons.
In Multiple Hypothesis Testing, prognostic group labels are randomly permuted.
Several authors have considered the problem of including weights in multiple hypothesis testing situations [ 31- 33].
False discovery rate (FDR) control is commonly accepted as the most appropriate error control in multiple hypothesis testing problems.
FDR (False Discovery Rate) control method [ 70] was used in multiple hypothesis testing to correct the results for p value.
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False discovery rate is a statistical method used in multiple hypotheses testing to correct for multiple comparisons.
Analysis was performed using paired Student t-tests with false discovery rate control, a statistical method used in multiple hypotheses testing to correct for multiple comparisons.
We also provide a false discovery rate control procedure [ 9], a well-known statistical methodology used in multiple hypotheses testing to correct for multiple comparisons.
Secondary metabolites, particularly phenolic compounds are often implicated in plant anti-herbivore defense mechanisms [ 42- 47], and have been considered important factors in multiple hypotheses of plant defense against herbivores [ 48- 50].
Calibrating confidence scores in multiple-hypothesis language recognition has been studied in [27].
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