|Date: ||Fri, 18 Feb 2005 13:18:45 -0800|
|Sender: ||"SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>|
|From: ||"David L. Cassell" <cassell.david@EPAMAIL.EPA.GOV>|
|Subject: ||Re: Estimate the parameters of a function|
|Content-type: ||text/plain; charset=US-ASCII|
Venita DePuy <depuy001@NOTES.DUKE.EDU> sagely replied:
> Unless you're talking about Proc Robustreg in v9 . .
> Haven't used it personally, but will do robust regression.
> Not sure if it's applicable to the subject at hand.
Excellent point (as I would expect from you).
PROC ROBUSTREG is *experimental* in SAS 9.0, and so you
do have to watch out for any surprises, like, say, a complete
failure with tracebacks showing up in your log file. (I
have not played enough with this proc to see this, but I
have seen fun like this with other experimental procs, like
PROC SURVEYFREQ in SAS 9.0 .)
PROC ROBUSTREG does M estimation (a la Huber) and Rousseeuw's
LTS estimation, as well as S estimates and MM estimates.
Our original poster would probably do better to try one of
these. (I would recommend starting off with LTS estimation if
he is worried about distribution contamination.) I'm still not
sure *why* our OP needed such an unusual minimization function.
The SAS Online Docs for PROC ROBUSTREG might provide him with
enough information that he could make some decisions on which
sort of approaches could give him the best protection from the
types of problems he is dealith with.
David Cassell, CSC
Senior computing specialist