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Related Experiment Video

Updated: Dec 13, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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RANK: Large-Scale Inference with Graphical Nonlinear Knockoffs.

Yingying Fan1, Emre Demirkaya1, Gaorong Li2

  • 1University of Southern California.

Journal of the American Statistical Association
|August 4, 2020
PubMed
Summary
This summary is machine-generated.

This study enhances the model-X knockoffs procedure for high-dimensional big data analysis. The new graphical nonlinear knockoffs (RANK) method ensures high statistical power and controls false discoveries, even with unknown covariate distributions.

Keywords:
Big dataGraphical nonlinear knockoffsHigh-dimensional nonlinear modelsLarge-scale inference and FDRPowerReproducibilityRobustness

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Area of Science:

  • Statistics
  • Machine Learning
  • Genomics

Background:

  • Reproducibility and power are critical for big data analysis in high-dimensional nonlinear models.
  • Existing knockoffs procedures require known covariate distributions, limiting their applicability.

Purpose of the Study:

  • To provide theoretical foundations for the power and robustness of the model-X knockoffs procedure in high-dimensional settings.
  • To introduce and validate a modified knockoffs method (RANK) for unknown covariate distributions.

Main Methods:

  • Theoretical analysis of the model-X knockoffs procedure under Gaussian graphical models.
  • Development and justification of the graphical nonlinear knockoffs (RANK) method.
  • Simulation studies and real data analysis to assess performance.

Main Results:

  • Established asymptotic power of one for the oracle knockoffs procedure with known distributions.
  • Demonstrated that the RANK method asymptotically controls the false discovery rate (FDR) and achieves asymptotic power of one with estimated distributions.
  • Simulation results show competitive performance of RANK against existing methods in FDR control and power.

Conclusions:

  • The study provides the first formal theoretical results on the power of knockoffs procedures.
  • The proposed RANK method offers a robust and powerful solution for feature selection in high-dimensional nonlinear models with unknown covariate distributions.
  • RANK shows promise for applications in big data and genomics.