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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