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: r-help-bounces at stat.math.ethz.ch
> [mailto:r-help-bounces at stat.math.ethz.ch] 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
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
>