A Minimax Optimal Ridge-Type Set Test for Global Hypothesis with Applications in Whole Genome Sequencing Association Studies.

ABSTRACT: Testing a global hypothesis for a set of variables is a fundamental problem in statistics with a wide range of applications. A few well-known classical tests include the Hotelling's T 2 test, the F test, and the empirical Bayes based score test. These classical tests, however, are not robust to the signal strength and could have a substantial loss of power when signals are weak or moderate, a situation we commonly encounter in contemporary applications. In this paper, we propose a Minimax Optimal Ridge-type Set Test (MORST), a simple and generic method for testing a global hypothesis. The power of MORST is robust and considerably higher than that of the classical tests when the strength of signals is weak or moderate. In the meantime, MORST only requires a slight increase in computation

SUBMITTER: Liu Y 

PROVIDER: S-EPMC11865954 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature