A factor-adjusted multiple testing of general alternatives

TL;DR

This paper introduces AdaFAT, a two-step factor-adjusted multiple testing procedure that effectively controls false discovery rate and achieves high power even with correlated tests and varying false proportions.

Contribution

The paper develops AdaFAT, a novel method for factor-adjusted multiple testing applicable to general alternatives, with theoretical guarantees and empirical validation.

Findings

AdaFAT controls false discovery rate effectively.

Power of AdaFAT approaches one under certain conditions.

Method performs well in simulations based on China A-share market data.

Abstract

Factor-adjusted multiple testing is used for handling strong correlated tests. Since most of previous works control the false discovery rate under sparse alternatives, we develop a two-step method, namely the AdaFAT, for any true false proportion. In this paper, the proposed procedure is adjusted by latent factor loadings. Under the existence of explanatory variables, a uniform convergence rate of the estimated factor loadings is given. We also show that the power of AdaFAT goes to one along with the controlled false discovery rate. The performance of the proposed procedure is examined through simulations calibrated by China A-share market.