The Terminating-Random Experiments Selector: Fast High-Dimensional Variable Selection with False Discovery Rate Control
This method addresses the need for fast and reliable variable selection in high-dimensional data, such as in genome-wide association studies, offering a practical tool with proven FDR guarantees.
The paper tackles the problem of high-dimensional variable selection with false discovery rate (FDR) control, proposing the T-Rex selector, which achieves FDR control at a target level while maximizing power and reducing computation time by over two orders of magnitude compared to benchmarks.
We propose the Terminating-Random Experiments (T-Rex) selector, a fast variable selection method for high-dimensional data. The T-Rex selector controls a user-defined target false discovery rate (FDR) while maximizing the number of selected variables. This is achieved by fusing the solutions of multiple early terminated random experiments. The experiments are conducted on a combination of the original predictors and multiple sets of randomly generated dummy predictors. A finite sample proof based on martingale theory for the FDR control property is provided. Numerical simulations confirm that the FDR is controlled at the target level while allowing for high power. We prove that the dummies can be sampled from any univariate probability distribution with finite expectation and variance. The computational complexity of the proposed method is linear in the number of variables. The T-Rex selector outperforms state-of-the-art methods for FDR control in numerical experiments and on a simulated genome-wide association study (GWAS), while its sequential computation time is more than two orders of magnitude lower than that of the strongest benchmark methods. The open source R package TRexSelector containing the implementation of the T-Rex selector is available on CRAN.