From: Amara Graps (Amara.Graps@mpi-hd.mpg.de)
Date: Sun Oct 14 2001 - 23:37:26 MDT
"Harvey Newstrom" <mail@HarveyNewstrom.com>:
> When I speak of "more noise", I mean we have more of a sample to analyze.
> If the "noise" is a hidden message, the larger percentage it takes up, the
> easier it becomes to detect. Increasing this kind of "noise" makes it more
> detectable if it is a message, or less likely to be a message if it does not
> become more detectable.
>
> I am merely saying that having more data makes statistical analysis easier.
James, Harvey etal:
Here's something that might be useful to the discussion. I heard a
Swiss fellow, Olivier Jaquet, present this paper last month:
"Stochastic modelling for time series reconstruction at active volcanoes"
by Olivier Jaquet (Colenco Power Engineering, Baden, Switzerland) and
Roberto Carniel (Departimento di Georisorse e Territorio, Universita
di Udine, Udine, Italy)
(It's not published yet.) The idea is to use the statistical
characteristics of the data to fill in gaps for modelling
purposes. Note: you are NOT creating *real* data (i.e. the folks who
need to make predictions based on real data should not use this). But
I think that this method has a valid use for those folks who can't
apply a data analysis on their data because of small gaps in the
time-series, or are trying to show long term dynamics of a system.
For example, the 'standard' wavelet transforms which determine
frequencies require as input time series data on an evenly-spaced time
grid.
The problem with kriging, or any classical interpolation method, is
that the method does not preserve the observed variability of the
data. In addition, those methods cannot usually be calibrated to the
analyzed observations. With this (which is what I think a much smarter
approach), the temporal auto-correlation is honored and the observed
variability of the data is conserved. A time series missing 20% of its
values can be reconstructed without gaps preserving its temporal
behavior in a statistical sense.
Amara
--
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Amara Graps, PhD | Max-Planck-Institut fuer Kernphysik
Heidelberg Cosmic Dust Group | Saupfercheckweg 1
+49-6221-516-543 | 69117 Heidelberg, GERMANY
Amara.Graps@mpi-hd.mpg.de * http://www.mpi-hd.mpg.de/dustgroup/~graps
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"Never fight an inanimate object." - P. J. O'Rourke
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