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Related Experiment Videos

Wavelet thresholding with bayesian false discovery rate control.

Mahlet G Tadesse1, Joseph G Ibrahim, Marina Vannucci

  • 1Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA. mtadesse@cceb.upenn.edu

Biometrics
|March 2, 2005
PubMed
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This study introduces a Bayesian approach to control the false discovery rate (FDR) in high-dimensional data analysis. The method uses posterior probabilities for hypothesis rejection, offering an alternative to traditional frequentist procedures.

Area of Science:

  • Statistics
  • Bayesian Inference
  • High-Dimensional Data Analysis

Background:

  • The false discovery rate (FDR) is widely used for multiplicity correction in high-dimensional data.
  • FDR has a natural Bayesian interpretation as the expected proportion of rejected null hypotheses.
  • Existing methods often rely on frequentist approaches.

Purpose of the Study:

  • To propose and evaluate a Bayesian approach for controlling the positive false discovery rate (pFDR).
  • To compare the performance of the proposed Bayesian method with the frequentist Benjamini and Hochberg procedure.
  • To demonstrate the application of the Bayesian FDR control in wavelet thresholding for signal recovery.

Main Methods:

  • Developing a Bayesian framework to control the positive FDR.

Related Experiment Videos

  • Utilizing posterior probabilities of null hypotheses for the rejection rule.
  • Applying the method to wavelet thresholding for signal extraction from noisy data.

    • Main Results:

    • The Bayesian approach provides a principled way to control pFDR.
    • Simulated data showed comparable or improved performance against the Benjamini and Hochberg procedure.
    • The method was successfully illustrated on nuclear magnetic resonance spectral data.

    Conclusions:

    • Bayesian FDR control offers a robust alternative for multiplicity correction.
    • The proposed method is effective in signal recovery tasks like wavelet thresholding.
    • This Bayesian framework extends the comparison between Bayesian and frequentist evidence measures in multiple testing.