Dear Martin,
 
I have been playing a lot with the glkerns() function in the "lokern"
package for "automatic" smoothing of time-series data.  This kernel
smoothing approach of Gasser and Mueller seems to perform quite well for
estimating the function and its derivatives (first and second derivatives).
In fact, this is one of the best methods based on my simulation studies for
comparing a number of "automatic" bandwidth selection methods.  I am
interested in applying this to automatic smoothing and feature extraction
for a "large" number (thousands) of time series, with hundreds of points per
time series.  This is where I am interested in seeing if the efficiency of
"glkerns" can be improved.  
 
Here follows my specific question:
 
You have to call glkerns() separately each time to compute the function and
its derivatives, ie. if I want the function and its first 2 derivatives, I
have to make 3 calls to glkerns().  This seems to me to be inefficient,
especially for large time series.  In smooth.spline(), for example, you can
call it once to get the fit, and then use the fitted object to compute the
derivatives using predict().  Is it possible to have a similar feature in
glkerns? 
 
Thanks for any suggestions.
 
Ravi.
 
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Ravi Varadhan, Ph.D.

Assistant Professor, The Center on Aging and Health

Division of Geriatric Medicine and Gerontology 

Johns Hopkins University

Ph: (410) 502-2619

Fax: (410) 614-9625

Email: rvaradhan@jhmi.edu

Webpage:
http://www.jhsph.edu/agingandhealth/People/Faculty_personal_pages/Varadhan.h
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