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Nonparametric procedures for partially paired data in two groups

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  • Harrar, Solomon W.
  • Feyasa, Merga B.
  • Wencheko, Eshetu

Abstract

A fully nonparametric method is developed for comparing samples with partially paired data. Partially-paired (correlated) data naturally arise, for example, as a result of missing values, in incomplete block designs or meta analysis. In the nonparametric setup, treatment effects are characterized in terms of functionals of distribution functions and the only assumption needed is that the marginal distributions are non-degenerate. The setup accommodates binary, ordered categorical, discrete and continuous data in a seamless fashion. The use of nonparametric effects addresses the Behrens–Fisher problem from the nonparametric point of view and allows construction of confidence intervals. Although the nonparametric methods are mainly asymptotic, methods for small sample approximations are also proposed. Size and power simulation results show numerical evidence of favorable performance of the nonparametric methods. The new nonparametric method has overwhelming power advantage when treatment effects are in the shape of the distributions and they perform comparably well with parametric methods for location-type alternatives. Data from randomized trials in public health are used to illustrate the application of the method.

Suggested Citation

  • Harrar, Solomon W. & Feyasa, Merga B. & Wencheko, Eshetu, 2020. "Nonparametric procedures for partially paired data in two groups," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
  • Handle: RePEc:eee:csdana:v:144:y:2020:i:c:s0167947319302580
    DOI: 10.1016/j.csda.2019.106903
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    References listed on IDEAS

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    1. Nicole Fuchs & Werner Pölz & Arne C. Bathke, 2017. "Confidence intervals for population means of partially paired observations," Statistical Papers, Springer, vol. 58(1), pages 35-51, March.
    2. Hani M. Samawi & Robert Vogel, 2014. "Notes on two sample tests for partially correlated (paired) data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 109-117, January.
    3. Akritas, Michael G. & Antoniou, Efi S. & Kuha, Jouni, 2006. "Nonparametric Analysis of Factorial Designs With Random Missingness: Bivariate Data," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1513-1526, December.
    4. Konietschke, F. & Harrar, S.W. & Lange, K. & Brunner, E., 2012. "Ranking procedures for matched pairs with missing data — Asymptotic theory and a small sample approximation," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1090-1102.
    5. Brunner, Edgar & Munzel, Ulrich & Puri, Madan L., 1999. "Rank-Score Tests in Factorial Designs with Repeated Measures," Journal of Multivariate Analysis, Elsevier, vol. 70(2), pages 286-317, August.
    6. Marc Hallin & Olivier Tribel, 2000. "The efficiency of some nonparametric competitors to correlogram-based methods," ULB Institutional Repository 2013/2159, ULB -- Universite Libre de Bruxelles.
    7. Edgar Brunner & Frank Konietschke & Markus Pauly & Madan L. Puri, 2017. "Rank-based procedures in factorial designs: hypotheses about non-parametric treatment effects," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1463-1485, November.
    8. Thompson, G. L., 1990. "Asymptotic distribution of rank statistics under dependencies with multivariate application," Journal of Multivariate Analysis, Elsevier, vol. 33(2), pages 183-211, May.
    9. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    Full references (including those not matched with items on IDEAS)

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