Knock-Off

Junwei Lu
Additional contact information
Junwei Lu: Harvard University

Chapter Chapter 21 in Big Data Analysis, 2025, pp 149-153 from Springer

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

Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-03161-7_21

Ordering information: This item can be ordered from
http://www.springer.com/9783032031617

DOI: 10.1007/978-3-032-03161-7_21

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().