control of the false discovery proportion for independently tested null hypotheses

Clicks: 264
ID: 153679
2012
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #11 of 40 articles by views in nature protocols

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Consider the multiple testing problem of testing m null hypotheses H1,…,Hm, among which m0 hypotheses are truly null. Given the P-values for each hypothesis, the question of interest is how to combine the P-values to find out which hypotheses are false nulls and possibly to make a statistical inference on m0. Benjamini and Hochberg proposed a classical procedure that can control the false discovery rate (FDR). The FDR control is a little bit unsatisfactory in that it only concerns the expectation of the false discovery proportion (FDP). The control of the actual random variable FDP has recently drawn much attention. For any level 1−α, this paper proposes a procedure to construct an upper prediction bound (UPB) for the FDP for a fixed rejection region. When 1−α=50%, our procedure is very close to the classical Benjamini and Hochberg procedure. Simultaneous UPBs for all rejection regions' FDPs and the upper confidence bound for the unknown m0 are presented consequently. This new proposed procedure works for finite samples and hence avoids the slow convergence problem of the asymptotic theory.
Reference Key
ge2012journalcontrol Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Yongchao Ge;Xiaochun Li
Journal nature protocols
Year 2012
DOI
10.1155/2012/320425
URL
http://dx.doi.org/10.1155/2012/320425
Keywords
probabilities mathematical statistics

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.