High-Dimensional False Discovery Rate Control for Dependent Variables
High-Dimensional False Discovery Rate Control for Dependent Variables
Algorithms that ensure reproducible findings from large-scale, high-dimensional data are pivotal in numerous signal processing applications. In recent years, multivariate false discovery rate (FDR) controlling methods have emerged, providing guarantees even in high-dimensional settings where the number of variables surpasses the number of samples. However, these methods often fail to …