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A simple data-driven fallback procedure for multiple comparisons

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  • Jared Wolf
  • Hong Zhou

Abstract

We propose a data-driven fallback procedure to combine the positive aspects of both data-driven multiple comparison procedures and those relying on predetermined strategies. The proposed procedure tests the hypotheses based on the ordering of p-values, but the significance level of the test at each sequential step is accumulated in the manner of the fallback procedure. It is proven that the new procedure strongly controls the familywise error rate and is uniformly more powerful than the weighted Holm procedure for more than two hypotheses. The simulation study shows that the new procedure is more powerful than the fallback in most cases.

Suggested Citation

  • Jared Wolf & Hong Zhou, 2021. "A simple data-driven fallback procedure for multiple comparisons," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(13), pages 3179-3197, July.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:13:p:3179-3197
    DOI: 10.1080/03610926.2019.1691231
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