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Applications of conditional power function of two-sample permutation test

Author

Listed:
  • Monjed H. Samuh

    (Palestine Polytechnic University)

  • Fortunato Pesarin

    (University of Padova)

Abstract

Permutation or randomization test is a nonparametric test in which the null distribution (distribution under the null hypothesis of no relationship or no effect) of the test statistic is attained by calculating the values of the test statistic overall permutations (or by considering a large number of random permutation) of the observed dataset. The power of permutation test evaluated based on the observed dataset is called conditional power. In this paper, the conditional power of permutation tests is reviewed. The use of the conditional power function for sample size estimation is investigated. Moreover, reproducibility and generalizability probabilities are defined. The use of these probabilities for sample size adjustment is shown. Finally, an illustration example is used.

Suggested Citation

  • Monjed H. Samuh & Fortunato Pesarin, 2018. "Applications of conditional power function of two-sample permutation test," Computational Statistics, Springer, vol. 33(4), pages 1847-1862, December.
    • Handle: RePEc:spr:compst:v:33:y:2018:i:4:d:10.1007_s00180-018-0803-1
    DOI: 10.1007/s00180-018-0803-1
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    References listed on IDEAS

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    1. Janie McDonald & Patrick D. Gerard & Christopher S. McMahan & William R. Schucany, 2016. "Exact-Permutation-Based Sign Tests for Clustered Binary Data Via Weighted and Unweighted Test Statistics," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(4), pages 698-712, December.
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    Cited by:

    1. Elyashiv Zangen & Shira Hadar & Christopher Lawrence & Mustafa Obeid & Hala Rasras & Ella Hanzin & Ori Aslan & Eyal Zur & Nadav Schulcz & Daniel Cohen-Hatab & Yona Samama & Sarah Nir & Yi Li & Irina D, 2024. "Prefrontal cortex neurons encode ambient light intensity differentially across regions and layers," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    2. Stefano Bonnini & Getnet Melak Assegie & Kamila Trzcinska, 2024. "Review about the Permutation Approach in Hypothesis Testing," Mathematics, MDPI, vol. 12(17), pages 1-29, August.

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