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Summary statistics knockoffs inference with family-wise error rate control.

Catherine Xinrui YuJiaqi GuZhaomeng ChenZihuai He
Published in: Biometrics (2024)
Testing multiple hypotheses of conditional independence with provable error rate control is a fundamental problem with various applications. To infer conditional independence with family-wise error rate (FWER) control when only summary statistics of marginal dependence are accessible, we adopt GhostKnockoff to directly generate knockoff copies of summary statistics and propose a new filter to select features conditionally dependent on the response. In addition, we develop a computationally efficient algorithm to greatly reduce the computational cost of knockoff copies generation without sacrificing power and FWER control. Experiments on simulated data and a real dataset of Alzheimer's disease genetics demonstrate the advantage of the proposed method over existing alternatives in both statistical power and computational efficiency.
Keyphrases
  • big data
  • mild cognitive impairment
  • artificial intelligence
  • data analysis