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Statistical Inference Based on Ranks

Published online by Cambridge University Press:  01 January 2025

Thomas P. Hettmansperger  and
Joseph W. McKean
Thomas P. Hettmansperger*
Affiliation:
The Pennsylvania State University
Joseph W. McKean
Affiliation:
The University of Texas, Dallas
*
Requests for reprints should be sent to Thomas P. Hettmansperger, Department of Statistics, Pond Lab., The Pennsylvania State University, University Park, Pennsylvania, 16802.
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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.

Keywords

robust estimatesefficient testsmultiple comparisonslinear modelsensitivity curves
Type
Original Paper
Information
Psychometrika , Volume 43 , Issue 1 , March 1978 , pp. 69 - 79
Copyright
Copyright © 1978 The Psychometric Society

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Footnotes

This research was supported in part by grant MCS76-07292 from the National Science Foundation.

References

Reference Note

Hettmansperger, T. P. & McKean, J. W. K step rank procedures in the linear model. Manuscript submitted for publication, 1977.Google Scholar

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