Permutation-based true discovery guarantee by sum tests

doi:10.1093/jrsssb/qkad019

Permutation-based true discovery guarantee by sum tests

2023-04-03

9

OA

AI

DOI：10.1093/jrsssb/qkad019

原文链接

原文求助

摘要

En 中文

Sum-based global tests are highly popular in multiple hypothesis testing. In this paper, we propose a general closed testing procedure for sum tests, which provides lower confidence bounds for the proportion of true discoveries (TDPs), simultaneously over all subsets of hypotheses. These simultaneous inferences come for free, i.e., without any adjustment of the alpha-level, whenever a global test is used. Our method allows for an exploratory approach, as simultaneity ensures control of the TDP even when the subset of interest is selected post hoc. It adapts to the unknown joint distribution of the data through permutation testing. Any sum test may be employed, depending on the desired power properties. We present an iterative shortcut for the closed testing procedure, based on the branch and bound algorithm, which converges to the full closed testing results, often after few iterations; even if it is stopped early, it controls the TDP. We compare the properties of different choices for the sum test through simulations, then we illustrate the feasibility of the method for high-dimensional data on brain imaging and genomics data.

Keyword:

closed testing

multiple testing

permutation test

selective inference

sum test

true discovery proportion

Permutation-based true discovery guarantee by sum tests

摘要

期刊

机构