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Permutation tests for mixed paired and two-sample designs

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Abstract

Permutation tests based on previously developed statistics are proposed for the case of mixed paired and two-sample designs. Different weighting schemes of previous tests are explored to understand the strengths and weaknesses of each test. A simulation study compares the power and Type I error rates of the new tests with those previously developed. Rank-based statistics generally performed as well as or better than parametric statistics, particularly for nonnormal distributions.

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References

  • Bhoj DS (1978) Testing equality of means of correlated variates with missing data on both responses. Biometrika 65:225–228

    Article  Google Scholar 

  • Bhoj DS (1984) On testing equality of variances of correlated variates with incomplete data. Biometrika 71:639–641

    Article  MathSciNet  Google Scholar 

  • Bhoj DS (1989) On comparing correlated means in the presence of incomplete data. Biom J 31:279–288

    Article  MathSciNet  Google Scholar 

  • Dubnicka SR, Blair RC, Hettmansperger TP (2002) Rank-based procedures for mixed paired and two-sample designs. J Mod Appl Stat Methods 1:32–41

    Article  Google Scholar 

  • Einsporn RL, Habtzghi D (2013) Combining paired and two-sample data using a permutation test. J Data Sci 11:767–779

    Article  MathSciNet  Google Scholar 

  • Hoaglin DC (2006) Summarizing shape numerically: the g-and-h distributions. In: Hoaglin DC, Mosteller F, Tukey JW (eds) Exploring data tables, trends, and shapes. John Wiley & Sons, Inc., Hoboken, NJ

  • Lin PE, Stivers LE (1974) On the difference of means with incomplete data. Biometrika 61:325–334

    Article  MathSciNet  Google Scholar 

  • Magel RC, Fu R (2014) Proposed nonparametric test for the mixed two-sample design. J Stat Theory Pract 8:221–237

    Article  MathSciNet  Google Scholar 

  • Maritz JS (1995) A permutation paired test allowing for missing values. Aust J Stat 37:153–159

    Article  Google Scholar 

  • Marozzi M (2014) Multivariate tests based on interpoint distances with application to magnetic resonance imaging. Stat Methods Med Res 25(6)

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Authors and Affiliations

  1. Wake Forest School of Medicine, Winston-Salem, NC, USA

    E. N. Johnson

  2. University of North Carolina at Greensboro, Greensboro, NC, USA

    S. J. Richter

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  1. E. N. Johnson
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  2. S. J. Richter
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Correspondence to S. J. Richter.

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Johnson, E.N., Richter, S.J. Permutation tests for mixed paired and two-sample designs. Comput Stat 37, 739–750 (2022). https://doi.org/10.1007/s00180-021-01137-9

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  • DOI: https://doi.org/10.1007/s00180-021-01137-9

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