2021/02/10 by Jiaqi Gu, Guosheng Yin, Gu, Jiaqi +1
Computer Science · Decision Sciences · Mathematics · #Advanced Statistical Methods and Models #FOS: Computer and information sciences #Forecasting Techniques and Applications #Gaussian Processes and Bayesian Inference #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2102.05223
openalex publication_date 2021/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In many scientific fields, researchers are interested in discovering features with substantial effect on the response from a large number of features while controlling the proportion of false discoveries. By incorporating the knockoff procedure in the Bayesian framework, we develop the Bayesian knockoff filter (BKF) for selecting features that have important effect on the response. In contrast to the fixed knockoff variables in a frequentist procedure, we allow the knockoff variables to be continuously updated using the Markov chain Monte Carlo. Based on the posterior samples and the elaborated greedy selection procedure, our method can distinguish the truly important features from unimportant ones and the Bayesian false discovery rate can be controlled at a desirable level. Numerical experiments on both synthetic and real data demonstrate the advantages of our BKF over existing knockoff methods and Bayesian variable selection approaches, i.e., the BKF possesses higher power and yields a lower false discovery rate.