I thought outlier tests were mainly superseded two decades ago by the use
of robust methods -- they certainly were in analytical chemistry, for
example. All outlier tests are "bad" in the sense that outliers will
damage the results long before they are detected. See e.g.
@Article{AMC.89a,
author = "{Analytical Methods Committee}",
title = "Robust statistics --- how not to reject outliers. {Part}
1. {Basic} concepts",
journal = "The Analyst",
volume = "114",
pages = "1693--1697",
year = "1989",
}
On Wed, 30 Jun 2004, Greg Tarpinian wrote:
> I have been learning about some outlier tests -- Dixon and Grubb,
> specifically -- for small data sets. When I try help.start() and search
> for outlier tests, the only response I manage to find is the Bonferroni
> test avaiable from the CAR package... are there any other packages the
> offer outlier tests?
That's not an outlier test in the sense used by Dixon and Grubb, but is an
illustration of the point about robust methods being better, in this case
protecting better against multiple outliers.
> Are the Dixon and Grubb tests "good" for small samples or are others
> more recommended?
--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595
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