A1686
Title: FDR control via neural networks under covariate-dependent symmetric nulls
Authors: Junyong Park - Seoul National University (Korea, South) [presenting]
Abstract: In modern multiple hypothesis testing, the availability of additional covariate information alongside the primary test statistics has motivated the development of more powerful and adaptive inference methods. However, most existing approaches rely on p-values that are precomputed under the assumption that their null distributions are independent of the covariates. A novel framework is proposed that first derives covariate-adaptive p-values from the assumption of symmetric null distribution of the primary variable given the covariates, without imposing any parametric assumptions. Building on these data-driven p-values, a neural network model is employed to learn a covariate-adaptive rejection threshold via the mirror estimation principle, optimizing the number of discoveries while maintaining valid false discovery rate (FDR) control. Furthermore, estimation of the conditional null distribution enables the computation of p-values directly from the raw data. Simulation studies and a real data example are presented.
