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Comparisons of rank-based methods and normal theory methods for unbalanced one-way repeated measures data

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Summary

In this article, rank-based methods are developed for analyzing unbalanced one-way repeated measures data. It is assumed that errors within each subject are exchangeable random variables.R-estimates of the treatment effects are obtained by minimizing a piecewise linear function. Rank-based methods are developed for testing the equality of the treatment effects. An unbalanced data set is analyzed for illustrative purposes. The relative efficiency of theR-estimates and maximum likelihood estimates is discussed. A small scale simulation study, comparing powers and sizes of the rank-based tests and normal theory tests (e.g., Wald and F), is presented.

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Authors and Affiliations

  1. Division of Biometrics II, HFD-715, Room 9B07, Office Biostatistics, CDER Food and Drug Administration, 5600 Fishers Lane, 20857, Rockville, MD, USA

    M. Mushfiqur Rashid

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  1. M. Mushfiqur Rashid
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The views expressed in this article are those of the author and not those of the United States Food and Drug Administration.

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Rashid, M.M. Comparisons of rank-based methods and normal theory methods for unbalanced one-way repeated measures data. J. Ital. Statist. Soc. 9, 199–218 (2000). https://doi.org/10.1007/BF03178966

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