Aggregation of Multiple Knockoffs
Abstract
We develop an extension of the knockoff inference procedure, introduced by Barber and Candès [2015]. This new method, called ag-gregation of multiple knockoffs (AKO), addresses the instability inherent to the random nature of knockoff-based inference. Specifically, AKO improves both the stability and power compared with the original knockoff algorithm while still maintaining guarantees for false discovery rate control. We provide a new inference procedure, prove its core properties , and demonstrate its benefits in a set of experiments on synthetic and real datasets.
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