Permutation Statistical Methods: Calculation Efficiencies

Kenneth J Berry, Janis E Johnston, Paul W Mielke, Howard W Mielke and Lindsay A Johnston
Additional contact information
Kenneth J Berry: Department of Sociology, Colorado State University, USA
Janis E Johnston: Department of Sociology, Colorado State University, USA
Paul W Mielke: 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

Biostatistics and Biometrics Open Access Journal, 2018, vol. 4, issue 3, 63-77

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.

Keywords: Biometrics Open Access Journal; Biostatistics and Biometrics; Biostatistics and Biometrics Open Access Journal; Open Access Journals; biometrics journal; biometrics articles; biometrics journal reference; biometrics journal impact factor; biometrics and biostatistics journal impact factor; journal of biometrics; open access juniper publishers; juniper publishers reivew (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:adp:jbboaj:v:4:y:2018:i:3:p:63-77

DOI: 10.19080/BBOAJ.2018.04.555640

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