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Marginal false discovery rates for penalized regression models.

Patrick J Breheny1

  • 1Department of Biostatistics, University of Iowa, Iowa City, IA, USA.

Biostatistics (Oxford, England)
|February 9, 2018
PubMed
Summary

This study introduces a method to assess feature selection reliability in high-dimensional data using marginal false discovery rates (mFDRs). The approach accurately estimates chance-based selections, aiding penalized regression analysis.

Keywords:
False discovery rateHigh-dimensional data analysisLassoPenalized regression

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Area of Science:

  • Statistics
  • Bioinformatics
  • Genomics

Background:

  • Penalized regression is valuable for high-dimensional data but lacks reliable feature selection inference.
  • Assessing the reliability of selected features is a significant challenge in these settings.

Purpose of the Study:

  • To develop and evaluate a method for assessing feature selection reliability in penalized regression using marginal false discovery rates (mFDRs).
  • To provide a practical tool for understanding how many feature selections are likely due to chance.

Main Methods:

  • Focuses on marginal false discovery rates (mFDRs) instead of classical, fully conditional false discoveries.
  • Employs theoretical analysis and simulation studies to validate the mFDR approach.
  • Applies the method to real-world gene expression and genome-wide association study data.

Main Results:

  • The mFDR approach provides a straightforward estimation of chance-based selections, summarizing selection reliability.
  • The method demonstrates high accuracy with mild predictor correlation and is only slightly conservative with stronger correlations.
  • The proposed method offers practical utility and advantages over existing approaches.

Conclusions:

  • Marginal false discovery rates offer a scalable and accurate solution for feature selection reliability in penalized regression for high-dimensional data.
  • The developed method is practical and advantageous for analyzing complex biological datasets.
  • This work facilitates more trustworthy inference in high-dimensional statistical modeling.