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Authors: Jeff T. Terpstra, Joseph W. McKean
Title: [download]
(8694)
Rank-Based Analysis of Linear Models Using R
Reference: Vol. 14, Issue 7, Jul 2005
Submitted 2004-04-06, Accepted 2005-07-01
Type: Article
Abstract:

It is well-known that Wilcoxon procedures out perform least squares procedures when the data deviate from normality and/or contain outliers. These procedures can be generalized by introducing weights; yielding so-called weighted Wilcoxon (WW) techniques. In this paper we demonstrate how WW-estimates can be calculated using an L1 regression routine. More importantly, we present a collection of functions that can be used to implement a robust analysis of a linear model based on WW-estimates. For instance, estimation, tests of linear hypotheses, residual analyses, and diagnostics to detect differences in fits for various weighting schemes are discussed. We analyze a regression model, designed experiment, and autoregressive time series model for the sake of illustration. We have chosen to implement the suite of functions using the R statistical software package. Because R is freely available and runs on multiple platforms, WW-estimation and associated inference is now universally accessible.

Paper: [download]
(8694)
Rank-Based Analysis of Linear Models Using R
(application/pdf, 675.4 KB)
Supplements: [download]
(1157)
wwcode.zip: wwcode
(application/x-zip-compressed, 43.4 KB)
[download]
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wwcode.zip: wwcode (Updated 1/14/07)
(application/x-zip-compressed, 44.7 KB)
Resources: BibTeX | OAI
Creative Commons License
This work is licensed under the licenses
Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3)
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