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Related Concept Videos

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Penalized likelihood and multiple testing.

Arthur Cohen1, John Kolassa1, Harold B Sackrowitz1

  • 1Department of Statistics and Biostatistics, Rutgers University, Hill Center, Piscataway, New Jersey, USA.

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|November 27, 2018
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Summary

This study introduces a novel multiple testing procedure inspired by the Bayesian Information Criterion (BIC). This generalized BIC method offers advantages for statistical models, especially sparse ones, outperforming existing approaches.

Keywords:
consistencyconvexityinformation criteriapairwise comparisonssparsity

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Area of Science:

  • Statistics
  • Statistical Modeling

Background:

  • The classical multiple testing model is a crucial area in statistics with ongoing development.
  • Inference after model selection problems often utilize penalized likelihood functions, such as the Bayesian Information Criterion (BIC).

Purpose of the Study:

  • To develop and evaluate a new multiple testing procedure based on a generalized Bayesian Information Criterion (BIC).
  • To assess the applicability and performance of the proposed method across various statistical models.

Main Methods:

  • Construction of a generalized BIC method.
  • Evaluation of the method's properties as a multiple testing procedure.
  • Application to diverse statistical models including regression, contrasts, treatment vs. control, and change point analysis.

Main Results:

  • The generalized BIC method is applicable to a wide range of statistical models.
  • Numerical evaluations suggest the new generalized BIC procedure is preferable to existing methods, particularly for sparse models.

Conclusions:

  • The developed generalized BIC method provides a valuable new approach to multiple testing.
  • This procedure demonstrates particular efficacy in sparse statistical models, offering an improvement over current techniques.