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Related Experiment Videos

Hommel's procedure in linear time.

Rosa J Meijer1, Thijmen J P Krebs2, Jelle J Goeman3

  • 1Statistics Netherlands, Postbus, HA Den Haag, The Netherlands.

Biometrical Journal. Biometrische Zeitschrift
|September 7, 2018
PubMed
Summary
This summary is machine-generated.

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This study introduces a new, exact statistical shortcut for familywise error rate control. It achieves linear time complexity on sorted P-values, improving upon existing methods for hypothesis testing.

Area of Science:

  • Statistics
  • Statistical hypothesis testing
  • Multiple comparisons

Background:

  • Familywise error rate (FWER) control is crucial in multiple hypothesis testing.
  • Existing exact methods like Hommel's procedure are computationally intensive (quadratic time).
  • Hochberg's procedure offers linear time but is conservative, leading to reduced statistical power.

Purpose of the Study:

  • To develop a novel shortcut procedure for FWER control.
  • To combine the exactness of Hommel's procedure with the linear time efficiency of Hochberg's procedure.
  • To extend the method to a robust variant of Hommel's procedure without the Simes inequality assumption.

Main Methods:

  • The study presents a new shortcut derived from a closed testing procedure using the Simes local test.
Keywords:
Hochbergclose testingconvex hullsimes

Related Experiment Videos

  • The proposed method operates in linear time complexity on sorted P-values.
  • A robust variant is developed, relaxing the Simes inequality requirement.

    • Main Results:

    • An exact shortcut procedure for FWER control is presented.
    • The new method achieves linear time complexity on sorted P-values, offering an improvement over Hommel's procedure.
    • The procedure is shown to be efficient and exact, outperforming conservative methods.

    Conclusions:

    • The novel shortcut provides an exact and computationally efficient solution for FWER control.
    • This method enhances statistical power by avoiding the conservativeness of existing linear-time procedures.
    • The approach is applicable to a broader range of statistical testing scenarios, including robust variants.