On False Discovery and Non-discovery Proportions of the Dynamic Adaptive Procedure

Kong Xin-Bing and Xu Qin-Feng

Scandinavian Journal of Statistics, 2015, vol. 42, issue 2, 530-544

Abstract: type="main" xml:id="sjos12121-abs-0001"> This paper studies the asymptotic behaviour of the false discovery and non-discovery proportions of the dynamic adaptive procedure under some dependence structure. A Bahadur-type representation of the cut point in simultaneously performing a large scale of tests is presented. The asymptotic bias decompositions of the false discovery and non-discovery proportions are given under some dependence structure. In addition to existing literatures, we find that the randomness due to the dynamic selection of the tuning parameter in estimating the true null rate serves as a source of the approximation error in the Bahadur representation and enters into the asymptotic bias term of the false discovery proportion and those of the false non-discovery proportion. The theory explains to some extent why some seemingly attractive dynamic adaptive procedures do not outperform the competing fixed adaptive procedures substantially in some situations. Simulations justify our theory and findings.

Date: 2015
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