Abstract
Application of the standard hypothesis test with no adjustment for the multiple testing leads to a large number of false discoveries. The most convenient error measure in multiple testing is False Discovery Rate, FDR. However, calculation of FDR requires good estimation of the number of true null hypotheses, n 0, (or equivalently, of π 0 = n 0/ n, where n denotes the number of all tested hypotheses). Estimation of π 0 is a non-trivial problem and it can be done under several assumptions about input data. In our study, several approaches to the estimation of π 0 values are compared for the different data models (independent features, block-correlated data and mixture models). The presented results give evidence that general dependence of the features leads to a very doubtful estimation of their significance.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
