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A complete procedure for testing a claim about a population proportion is provided here.
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Estimating False Discovery Proportion Under Arbitrary Covariance Dependence.

Jianqing Fan1, Xu Han2, Weijie Gu3

  • 1Department of Operations Research & Financial Engineering, Princeton University, Princeton, NJ 08544, USA and honorary professor, School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China.

Journal of the American Statistical Association
|April 15, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for multiple hypothesis testing, effectively managing correlations in large datasets. The approach improves false discovery control in fields like genomics, offering a more powerful alternative to existing methods.

Keywords:
Multiple hypothesis testingarbitrary dependence structurefalse discovery rategenome-wide association studieshigh dimensional inference

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Area of Science:

  • Statistics
  • Bioinformatics
  • Genomics

Background:

  • Multiple hypothesis testing is crucial in high-dimensional inference, particularly in genome-wide association studies (GWAS).
  • Controlling false discoveries is challenging when numerous statistical tests are correlated.
  • Arbitrary dependence structures complicate standard false discovery rate (FDR) control methods.

Purpose of the Study:

  • To develop a novel method for robust false discovery control under arbitrary dependence structures in large-scale multiple testing.
  • To address the challenges posed by correlated test statistics in fields like genomics.

Main Methods:

  • A novel method based on principal factor approximation is proposed.
  • This method subtracts common dependence and weakens the correlation structure among test statistics.
  • An approximate expression for false discovery proportion (FDP) is derived, and a consistent estimate of realized FDP is provided.

Main Results:

  • The proposed method effectively handles arbitrary dependence structures.
  • The derived FDP estimate is consistent and useful for controlling FDR and FDP.
  • The method's performance in estimating realized FDP favorably compares to Efron (2007).

Conclusions:

  • The principal factor approximation method offers a significant advancement in managing correlated tests in high-dimensional inference.
  • The developed FDP estimation provides a reliable tool for FDR control in large-scale studies.
  • A dependence-adjusted procedure is introduced, demonstrating increased power compared to fixed threshold methods.