footnode.html
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<DL> <DT><A NAME="41">...variance.</A><DD>
In order to simplify the notation in mathematical derivations, we will
assume throughout this paper that the mean of each time series has been
subtracted and it has been rescaled to unit variance. Nevertheless, we will
often transform back to the original experimental units when displaying
results graphically.
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</PRE><DT><A NAME="50">...quantity</A><DD>We have omitted the commonly used normalisation to second moments
since throughout this paper, time series and their surrogates will have the
same second order properties and identical pre-factors do not enter the
tests.
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</PRE><DT><A NAME="220">...data,</A><DD>
Formally, digitisation is a non-invertible, nonlinear measurement and thus
not included in the null hypothesis. Constraining the surrogates to take
exactly the same (discrete) values as the data seems to be reasonably safe,
though. Since for that case we haven't seen any dubious rejections due to
discretisation, we didn't discuss this issue as a serious caveat. This
decision may of course prove premature.
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</PRE><DT><A NAME="539">...here.</A><DD>
Thanks to Bruce Gluckman for pointing this out to us.
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</PRE><DT><A NAME="982">...chain.</A><DD>
Contrary to what is said in Ref. [<A HREF="node36.html#witt" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/node36.html#witt">24</A>], binning a two dimensional
distribution yields a first order (rather than a second order) Markov
process, for which a three dimensional binning would be needed to include
the image distribution as well.
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</PRE> </DL>
<P><ADDRESS>
<I>Thomas Schreiber <BR>
Mon Aug 30 17:31:48 CEST 1999</I>
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