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We will continue the discussion of controlling the false discovery rate (FDR). When testing null hypotheses \(\{H_{0j}\}_{j=1}^d\) , recall that the FDR is defined as \(\displaystyle \text{FDR} = \mathbb {E} \bigg ( \frac {\# \text{False Positives}}{\# \text{Rejected Hypotheses}} \bigg ). \) In the previous chapter, we discussed the case where the p-values corresponding to the \(\{H_{0j}\}_{j=1}^d\) were independent. Here, we consider the more challenging case where the p-values are dependent.

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Knock-Off

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  • 发表日期：

    2025年

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  • Junwei Lu

摘要

We will continue the discussion of controlling the false discovery rate (FDR). When testing null hypotheses \(\{H_{0j}\}_{j=1}^d\) , recall that the FDR is defined as \(\displaystyle \text{FDR} = \mathbb {E} \bigg ( \frac {\# \text{False Positives}}{\# \text{Rejected Hypotheses}} \bigg ). \) In the previous chapter, we discussed the case where the p-values corresponding to the \(\{H_{0j}\}_{j=1}^d\) were independent. Here, we consider the more challenging case where the p-values are dependent.