False discovery rate for scanning statistics

D. O. Siegmund*, N. R. Zhang, B. Yakir

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


The false discovery rate is a criterion for controlling Type I error in simultaneous testing of multiple hypotheses. For scanning statistics, due to local dependence, clusters of neighbouring hypotheses are likely to be rejected together. In such situations, it is more intuitive and informative to group neighbouring rejections together and count them as a single discovery, with the false discovery rate defined as the proportion of clusters that are falsely declared among all declared clusters. Assuming that the number of false discoveries, under this broader definition of a discovery, is approximately Poisson and independent of the number of true discoveries, we examine approaches for estimating and controlling the false discovery rate, and provide examples from biological applications.

Original languageAmerican English
Pages (from-to)979-985
Number of pages7
Issue number4
StatePublished - Dec 2011

Bibliographical note

Funding Information:
The research of the first and third authors is supported by the Israeli-American Bi-National Fund. The second and third authors are supported by the National Science Foundation, U.S.A. We would like to thank


  • False discovery rate
  • Multiple comparisons
  • Poisson approximation
  • Scan statistic


Dive into the research topics of 'False discovery rate for scanning statistics'. Together they form a unique fingerprint.

Cite this