Abstract
This paper develops a unified approach, based on ranks, to the statistical analysis of data arising from complex experimental designs. In this way we answer a major objection to the use of rank procedures as a major methodology in data analysis. We show that the rank procedures, including testing, estimation and multiple comparisons, are generated in a natural way from a robust measure of scale. The rank methods closely parallel the familiar methods of least squares, so that estimates and tests have natural interpretations.
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Hettmansperger, T. P. & McKean, J. W.K step rank procedures in the linear model. Manuscript submitted for publication, 1977.
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This research was supported in part by grant MCS76-07292 from the National Science Foundation.
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Hettmansperger, T.P., McKean, J.W. Statistical inference based on ranks. Psychometrika 43, 69–79 (1978). https://doi.org/10.1007/BF02294090
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DOI: https://doi.org/10.1007/BF02294090