Dear Seba,
As far as I know, the shape of the kernel is not that important. Any kernel will yield approximately the same results as long as the scaling parameters (bandwidth) are equivalent. There are some theoretical as well as practical reasons for choosing finite
(compact) kernels - the practical reasons for big/huge problems become very important ones.
José M.

En/na seba ha escrit:

Maybe I'm getting old...there was something wrong also in the past mail!
Please consider this one.
Sorry for the wrong mails.


Dear list members

I'm always fighting with the decomposition of trend and residuals or more generally I need to decompose
my signal in high and low frequency variability. Without coming to wavelets techniques a simple
way is to use moving window averages to obtain a smoothed signal and removing it from the original
signal to obtain the residuals. In this way the size of the window let you choose at which level of detail to
perform the analysis, i.e. smaller the window higher the frequency of the signal you want to study.

But working with moving averages I realize (well, I know that this is not so a big new!) that performing
moving window simply doing averages gives a smoothed signal that has some noise; differently if a use some kind
of kernel function (also very simple such the one used by Grigov et al.  "geostatistical Mapping with continuos moving neighborhood", mathematical geology vol 36 no. 2, 2004 see page 273), things work really better (and using a kernel of that type is like calculating moving windows averages not on the original signal but iteratively on moving averages calculated in smaller windows....).

Now my question is: which is the reason for choosing a specific shape of the kernel????

Sebastiano


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-- 
---------------------------------------
José M. Blanco-Moreno

Dept. de Biologia Vegetal (Botànica)
Facultat de Biologia
Universitat de Barcelona
Av. Diagonal 645
08028 Barcelona
SPAIN
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phone: (+34) 934 039 863
fax: (+34) 934 112 842
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