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PDC-MAKES: a conditional screening method for controlling false discoveries in high-dimensional multi-response

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This summary is machine-generated.

This study introduces a new model-free method for identifying important predictors in complex datasets. It effectively controls the false discovery rate (FDR) even with high-dimensional and correlated data.

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Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • High dimensionality and strong correlations in data present challenges for identifying key predictors.
  • Existing methods struggle with multi-response settings and complex data structures.

Purpose of the Study:

  • To propose a model-free conditional feature screening method for ultrahigh-dimensional, multi-response data.
  • To control the false discovery rate (FDR) while identifying important predictors.

Main Methods:

  • Utilizing partial distance correlation to measure dependence between random vectors, controlling for multivariate effects.
  • Employing derandomized knockoff-e-values for stable thresholding and false discovery rate control.
  • Developing a robust screening approach for heavy-tailed data and correlated predictors.

Main Results:

  • The method identifies predictors that are marginally unrelated but conditionally related to the response.
  • Achieves sure screening property, maintains FDR control, and offers higher statistical power.
  • Demonstrates robustness against heavy-tailed distributions and high predictor correlations.

Conclusions:

  • The proposed method offers a powerful and robust approach for feature screening in ultrahigh-dimensional settings.
  • It advances current research by allowing high-dimensional variables to be conditioned upon.
  • The method shows superior performance in simulations and a real-world data application.