Abstract
ABSTRACTMultiple comparison, which tests tens of thousands hypotheses simultaneously, has been developed extensively under the assumption of independence of test statistics. Practically the independence assumption, without which multiple comparison inference becomes very challenging, may not be suitable. Fan, Han and Gu proposed an efficient way to estimate the false discovery proportion when the test statistics are normally distributed with arbitrary covariance dependence. The normal distribution assumption constrains the employment of their estimator. In this article, we generalize their method to the cases where the test statistics are built on the normally distributed sample.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Communications in Statistics - Simulation and Computation
Disclaimer: 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.