当前位置: X-MOL 学术J. Stat. Plann. Inference › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Adapting to one- and two-way classified structures of hypotheses while controlling the false discovery rate
Journal of Statistical Planning and Inference ( IF 0.9 ) Pub Date : 2021-02-27 , DOI: 10.1016/j.jspi.2021.02.006
Shinjini Nandi , Sanat K. Sarkar , Xiongzhi Chen

There is ample research on false discovery rate (FDR) control for testing hypotheses classified according to one criterion. However, scenarios of hypotheses partitioned via two different criteria are often encountered in practice. Such two-way classification encodes more structural information in the associated multiple testing of the hypotheses than its one-way or un-classified counterparts. Unfortunately, there seems to be very little research tailored for multiple testing under that classification setting. This article proposes weighted versions of the Benjamini–Hochberg (BH) method, both in their oracle and data-adaptive forms, efficiently capturing one- or two-way classified structure of hypotheses through appropriately chosen weights. The proposed methods control FDR non-asymptotically in their oracle forms under positive regression dependence on subset (PRDS) of null p-values and in their data-adaptive forms for independent p-values. The one-way data-adaptive methods are asymptotically conservative under weak dependence. Simulations demonstrate these methods’ superior power performances over some contemporary procedures and provide evidence of their non-asymptotic conservativeness under certain dependence scenarios. The proposed two-way adaptive procedure is effectively applied to a data set from microbial abundance study.



中文翻译:

在控制错误发现率的同时适应假设的单向和双向分类结构

关于错误发现率(FDR)控制的大量研究,用于检验根据一种标准分类的假设。但是,在实践中经常会遇到通过两个不同的标准划分假设的情况。与单向或未分类的对应方法相比,这种双向分类在假设的关联多重检验中编码的结构信息更多。不幸的是,在这种分类背景下,针对多种测试量身定制的研究似乎很少。本文提出的的Benjamini-Hochberg的(BH)方法的加权的版本中,无论是在其Oracle和数据自适应形式,通过适当地选择权重有效地捕获单向或双向假设的分类的结构。所提出的方法在对空p值的子集(PRDS)的正回归依赖下以及对独立p值的数据自适应形式下,以其oracle形式非渐进地控制FDR。单向数据自适应方法在弱依赖性下是渐近保守的。仿真证明了这些方法在某些当代程序上的优越功率性能,并提供了在某些依赖情况下其非渐进保守性的证据。所提出的双向自适应程序可有效地应用于微生物丰度研究的数据集。仿真证明了这些方法在某些当代程序上的优越功率性能,并提供了在某些依赖情况下其非渐进保守性的证据。所提出的双向自适应程序可有效地应用于微生物丰度研究的数据集。仿真证明了这些方法在某些当代程序上的优越功率性能,并提供了在某些依赖情况下其非渐进保守性的证据。所提出的双向自适应程序可有效地应用于微生物丰度研究的数据集。

更新日期:2021-03-15
down
wechat
bug