Linear models: permutation methods
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By: B.S. Cade
Edited by: B.S. Everitt and D.C. Howell
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Abstract
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]...
| 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 |
| Google Analytic Metrics | Metrics page |