Rank-Based Analysis of Linear Models Using R

Jeff T. Terpstra and Joseph W. McKean

Journal of Statistical Software, 2005, vol. 014, issue i07

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.

Date: 2005-07-01
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:014:i07

DOI: 10.18637/jss.v014.i07

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