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<H2><A NAME="SECTION00033000000000000000">Test design</A></H2>
<P>
Before we go into detail about the generation of surrogate samples, let us
outline how an actual test can be carried out.  Many examples are known of
nonlinearity measures that aren't even approximately normally distributed. It
has therefore been advocated since the early days&nbsp;[<A HREF="node36.html#theiler1" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/node36.html#theiler1">6</A>] to use robust
statistics rather than parametric methods for the actual statistical test. In
other words, we discourage the common practice to represent the distribution of
the nonlinearity measure by an error bar and deriving the significance from the
number of ``sigmas'' the data lies outside these bounds. Such a reasoning
implicitly assumes a Gaussian distribution.
<P>
Instead, we follow Theiler et al.&nbsp;[<A HREF="node36.html#theiler1" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/node36.html#theiler1">6</A>] by using a rank-order test.
First, we select a residual probability <IMG WIDTH=10 HEIGHT=7 ALIGN=BOTTOM ALT="tex2html_wrap_inline1914" SRC="img7.gif" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/img7.gif"> of a false rejection,
corresponding to a level of significance <IMG WIDTH=104 HEIGHT=24 ALIGN=MIDDLE ALT="tex2html_wrap_inline1932" SRC="img12.gif" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/img12.gif">.  Then, for a
one-sided test (e.g. looking for <EM>small</EM> prediction errors only), we
generate <IMG WIDTH=90 HEIGHT=24 ALIGN=MIDDLE ALT="tex2html_wrap_inline1934" SRC="img13.gif" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/img13.gif"> surrogate sequences. Thus, including the data itself,
we have <IMG WIDTH=25 HEIGHT=24 ALIGN=MIDDLE ALT="tex2html_wrap_inline1936" SRC="img14.gif" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/img14.gif"> sets. Therefore, the probability that the data by
coincidence has the smallest, say, prediction error is exactly <IMG WIDTH=10 HEIGHT=7 ALIGN=BOTTOM ALT="tex2html_wrap_inline1914" SRC="img7.gif" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/img7.gif">, as
desired.  For a two-sided test (e.g. for time asymmetry which can go both
ways), we would generate <IMG WIDTH=90 HEIGHT=24 ALIGN=MIDDLE ALT="tex2html_wrap_inline1940" SRC="img15.gif" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/img15.gif"> surrogates, resulting in a probability
<IMG WIDTH=10 HEIGHT=7 ALIGN=BOTTOM ALT="tex2html_wrap_inline1914" SRC="img7.gif" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/img7.gif"> that the data gives <EM>either</EM> the smallest <EM>or</EM> the largest
value.
<P>
For a minimal significance requirement of 95% , we thus need at least 19 or 39
surrogate time series for one- and two-sided tests, respectively. The
conditions for rank based tests with more samples can be easily worked out.
Using more surrogates can increase the discrimination power.
<P>
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<P><ADDRESS>
<I>Thomas Schreiber <BR>
Mon Aug 30 17:31:48 CEST 1999</I>
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