[R] pros and cons of "robust regression"? (i.e. rlm vs lm)

[Berton Gunter]

There is a **Huge** literature on robust regression, including many books
that you can search on at e.g. Amazon. I think it fair to say that we have
known since at least the 1970's that practically any robust downweighting
procedure (see, e.g "M-estimation") is preferable (more efficient, better
continuity properties, better estimates) to trimming "outliers" defined by
arbitrary threshholds. An excellent but now probably dated introductory
discussion can be found in "UNDERSTANDING ROBUST AND EXPLORATORY DATA
ANALYSIS" edited by Hoaglin, Tukey, Mosteller, et. al.

The rub in all this is that nice small sample inference results go our the
window, though bootstrapping can help with this. Nevertheless, for a variety
of reasons, my recommendation is simply to **never** use lm and **always**
use rlm (with maybe a few minor caveats). Many would disagree with this,
however.

I don't think "normalizing" data as it's conventionally used has anything to
do with robust regression, btw.

-- Bert Gunter
Genentech Non-Clinical Statistics
South San Francisco, CA
 
"The business of the statistician is to catalyze the scientific learning
process."  - George E. P. Box
 
 

> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch 
> [mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of r user
> Sent: Thursday, April 06, 2006 8:51 AM
> To: rhelp
> Subject: [R] pros and cons of "robust regression"? (i.e. rlm vs lm)
> 
> Can anyone comment or point me to a discussion of the
> pros and cons of robust regressions, vs. a more
> "manual" approach to trimming outliers and/or
> "normalizing" data used in regression analysis?
> 
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