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: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] 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? > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
