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Permutation Statistical Methods: Calculation Efficiencies

Author

Listed:
  • Kenneth J Berry

    (Department of Sociology, Colorado State University, USA)

  • Janis E Johnston

    (Department of Sociology, Colorado State University, USA)

  • Paul W Mielke Jr

    (Department of Statistics, Colorado State University, Fort Collins, USA)

  • Howard W Mielke

    (Department of Pharmacology, Tulane University, USA)

  • Lindsay A Johnston

    (US Air Force, JBER-Elmendorf Family Health Clinic, USA)

Abstract

Permutation statistical methods have much to recommend them as they are distribution-free, appropriate for non-random samples, and provide exact probability values. However, permutation statistical methods, by their very design, are computationally intensive. Eight calculation efficiencies for permutation statistical methods are presented and illustrated with example data sets.

Suggested Citation

  • Kenneth J Berry & Janis E Johnston & Paul W Mielke Jr & Howard W Mielke & Lindsay A Johnston, 2018. "Permutation Statistical Methods: Calculation Efficiencies," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 4(3), pages 63-77, January.
    • Handle: RePEc:adp:jbboaj:v:4:y:2018:i:3:p:63-77
    DOI: 10.19080/BBOAJ.2018.04.555640
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    Cited by:

    1. Oladugba Abimibola Victoria & Obasi Ajali John & Asogwa Oluchukwu Chukwuemeka, 2022. "Robustness of randomisation tests as alternative analysis methods for repeated measures design," Statistics in Transition New Series, Polish Statistical Association, vol. 23(4), pages 77-90, December.

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