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Related Experiment Videos

Quantile-function based null distribution in resampling based multiple testing.

Mark J van der Laan1, Alan E Hubbard

  • 1Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA. laan@stat.berkeley.edu

Statistical Applications in Genetics and Molecular Biology
|October 20, 2006
PubMed
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This study introduces a novel resampling method for multiple hypothesis testing, improving type-I error control and statistical power in data analysis. The new approach offers more accurate results in finite samples compared to existing methods.

Area of Science:

  • Statistics
  • Statistical inference
  • Multiple hypothesis testing

Background:

  • Simultaneously testing multiple null hypotheses is crucial in many statistical applications.
  • Existing methods using marginal p-values can be conservative or rely on unverified assumptions.
  • Prior resampling methods had limitations, such as subset pivotality conditions or asymptotic validity.

Purpose of the Study:

  • To propose a new resampling-based multiple testing method with a generally asymptotically valid null distribution.
  • To develop a bootstrap estimate for this null distribution with user-supplied marginal distributions.
  • To improve type-I error control and statistical power in finite samples.

Main Methods:

  • Developed a new generally asymptotically valid null distribution for test-statistics.

Related Experiment Videos

  • Proposed a corresponding bootstrap estimate with user-supplied marginal distributions.
  • Evaluated the method's performance through simulation and data analysis.

    • Main Results:

    • The new method provides more accurate control of Type-I error in finite samples.
    • Demonstrated increased statistical power compared to previous approaches.
    • Formal results established the theoretical validity and practical performance.

    Conclusions:

    • The proposed resampling method enhances multiple hypothesis testing accuracy and power.
    • This approach overcomes limitations of prior methods regarding null distribution assumptions.
    • Offers a more robust and powerful tool for statistical inference in various applications.