Aggregation of Multiple Knockoffs

• URL: http://arxiv.org/abs/2002.09269v2
• Date: Thu, 25 Jun 2020 14:26:21 GMT
• Title: Aggregation of Multiple Knockoffs
• Authors: Tuan-Binh Nguyen, J\'er\^ome-Alexis Chevalier, Bertrand Thirion, Sylvain Arlot
• Abstract summary: Aggregation of Multiple Knockoffs (AKO) addresses the instability inherent to the random nature of Knockoff-based inference. 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.
• Score: 33.79737923562146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop an extension of the Knockoff Inference procedure, introduced by Barber and Candes (2015). This new method, called Aggregation 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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