SyNPar: Synthetic Null Data Parallelism for High-Power False Discovery Rate Control in High-Dimensional Variable Selection

Music City Center 

Balancing false discovery rate (FDR) control and statistical power is a fundamental challenge in high-dimensional variable selection. Existing FDR control methods often perturb the original data, either by concatenating knockoffs variables or splitting the data, which can compromise power. In this paper, we introduce SyNPar, a novel framework that controls the FDR in high-dimensional variable selection while preserving the integrity of the original data. The framework is versatile, straightforward to implement, and applicable to a wide range of statistical models, including high-dimensional linear regression, generalized linear models (GLMs), Cox models, and Gaussian graphical models. Through extensive simulations and real-world data applications, we demonstrate that SyNPar consistently outperforms state-of-the-art methods, such as knockoffs and data-splitting techniques, in terms of FDR control, statistical power, and computational efficiency.