Linear models: permutation methods

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Edited by: B.S. Everitt and D.C. Howell



Permutation tests (see Permutation Based Inference) for the linear model have applications in behavioral studies when traditional parametric assumptions about the error term in a linear model are not tenable. Improved validity of Type I error rates can be achieved with properly constructed permutation tests. Perhaps more importantly, increased statistical power, improved robustness to effects of outliers, and detection of alternative distributional differences can be achieved by coupling permutation inference with alternative linear model estimators. For example, it is well-known that estimates of the mean in linear model are extremely sensitive to even a single outlying value of the dependent variable compared to estimates of the median [7, 19]. Traditionally, linear modeling focused on estimating changes in the center of distributions (means or medians). However, quantile regression allows distributional changes to be estimated in all or any selected part of a distribution or responses, providing a more complete statistical picture that has relevance to many biological questions [6]...

Additional publication details

Publication type Book chapter
Publication Subtype Book Chapter
Title Linear models: permutation methods
DOI 10.1002/0470013192.bsa479
Volume 2
Year Published 2005
Language English
Publisher John Wiley & Sons, Ltd.
Publisher location Chichester, West Sussex, UK
Contributing office(s) Fort Collins Science Center
Description p. 1049-1054
Larger Work Type Book
Larger Work Subtype Other Government Series
Larger Work Title Encyclopedia of statistics in behavioral science, volume 2
First page 1,049
Last page 1,054
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