Note:
As I believe Brian Ripley pointed out in his MASS book, loess may not be as
resistant to outliers (which is one aspect of robustness; robustness of
efficiency is another) as you think. The problem is that it starts off with
LS estimates and these can be so distorted by unusual values that the
reweighting cannot properly recover; i.e. convergence is to a local minimum
far from the desired global one. You might wish to read the documentation
for rlm() (in MASS, the package) and the appropriate sections of MASS, the
book.
Cheers,
-- 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 Marta Colombo
Sent: Monday, November 28, 2005 10:38 AM
To: R help
Subject: [R] Robust fitting
Good evening,I am Marta Colombo, student of Politecnico di
Milano. I'm studying Local Regression Techniques such as
loess, smoothing splines and kernel smoothers. Choosing
symmetric for the argument family in loess function it is
possible to produce a robust estimate , in function
smooth.spline and ksmooth I didn't find this possibility.
Well, is there a way to produce a robust estimate using
smoothing splines or kernel smoothers? And if the answer is
no, why? I'm asking these questions because I need to know
loess' advantages and disadvantages compared to other
techniques. Thank you very much for attention,
Marta Colombo
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