grubbs.test

The Grubbs test is one of many old (1950's - '70's) and classical tests for
outliers in linear regression. Here's a link:
http://www.itl.nist.gov/div898/handbook/eda/section3/eda35h.htm

I think it fair to say that such outlier detection methods were long ago
found to be deficient and have poor statistical properties and were
supplanted by (computationally much more demanding -- but who cares these
days!?) robust/resistant techniques, at least in the more straightforward
linear models contexts. rlm() in MASS (the package) is one good
implementation of these ideas in R. See MASS (the book by V&R) for a short
but informative discussion and further references.

I should add that the use of robust/resistant techniques exposes (i.e., they
exist but we statisticians get nervous talking publicly about them) many
fundamental issues about estimation vs inference, statistical modeling
strategies, etc. The problem is that important estimation and inference
issues for R/R estimators remain to be worked out -- if, indeed, it makes
sense to think about things this way at all. For example, for various kinds
of mixed effects models, "statistical learning theory" ensemble methods,
etc. The problem, as always, is what the heck does one mean by "outlier" in
these contexts. Seems to be like pornography -- "I know it when I see it."*

Contrary views cheerfully solicited!

Cheers to all,

-- Bert Gunter

*Sorry -- that's a reference to a famous quote of Justice Potter Stewart, an
American Supreme Court Justice.
http://www.michaelariens.com/ConLaw/justices/stewart.htm

grubbs.test

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