Estimating false discovery proportion in multiple comparison under dependency. Issue 9 (21st October 2017) - Zhao, Haibing
ABSTRACT: Multiple 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.