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Re: st: outliers


From   Steve Samuels <sjsamuels@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: outliers
Date   Sun, 22 Aug 2010 22:25:02 -0400

There are few rules about outliers, but the most important one is: OLS
is the worst way to detect them. Detection requires a robust
regression program; and a good program will not "reject" all outliers,
but will automatically downweight them.  For covariates, one wants to
identify not outliers per se, but those with high leverage.  But the
decision about what to do with these is not automatic; sometimes they
are the most important points and _must_ be kept.

See: "Robust regression in Stata" by Vincenzo Verardi and Christophe
Croux, The Stata Journal
Volume 9 Number 3: pp. 439-453. Also available at:
https://lirias.kuleuven.be/bitstream/123456789/202142/1/KBI_0823.pdf

See also Verardi and Croux's contributed programs -mmregress- (findit)
and Ben Jann's -robreg- (findit). These are superior to Stata's
long-time built-in command -rreg-.

Steve

Steven Samuels
sjsamuels@gmail.com
18 Cantine's Island
Saugerties NY 12477
USA
Voice: 845-246-0774
Fax:    206-202-4783

On Sun, Aug 22, 2010 at 4:04 PM, Fabio Zona <fabio.zona@unibocconi.it> wrote:

> in a OLS model, can I limit the analysis on outliers related to the predictors only? Or do I have to check for eventual outliers also for control variables?

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