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<H2><A NAME="SECTION00042000000000000000">Flatness bias of AAFT surrogates</A></H2>
<P>
It is argued in Ref.&nbsp;[<A HREF="node36.html#surrowe" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/node36.html#surrowe">30</A>] that for short and strongly correlated
sequences, the AAFT algorithm can yield an incorrect test since it introduces a
bias towards a slightly flatter spectrum. In Fig.&nbsp;<A HREF="node11.html#figflat" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/node11.html#figflat">3</A> we see power
spectral estimates of a clinical data set and of 19 AAFT surrogates.  The data
is taken from data set B of the Santa Fe Institute time series
contest&nbsp;[<A HREF="node36.html#gold" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/node36.html#gold">31</A>]. It consists of 4096 samples of the breath rate of a
patient with sleep apnoea. The sampling interval is 0.5&nbsp;seconds. The
discrepancy of the spectra is significant. A bias towards a white spectrum is
noted: power is taken away from the main peak to enhance the low and high
frequencies.
<P>
<blockquote><A NAME="926">&#160;</A><IMG WIDTH=349 HEIGHT=186 ALIGN=BOTTOM ALT="figure1037" SRC="img31.gif" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/img31.gif"><BR>
<STRONG>Figure:</STRONG> <A NAME="figflat">&#160;</A>
   Discrepancy of the power spectra of human breath rate data (solid
   line) and 19 AAFT surrogates (dashed lines). Here the power spectra have
   been  computed with a square window of length&nbsp;64.<BR>
</blockquote>
<P>
Heuristically, the flatness bias can be understood as follows. Amplitude
adjustment attempts to invert the unknown measurement function <IMG WIDTH=22 HEIGHT=24 ALIGN=MIDDLE ALT="tex2html_wrap_inline1908" SRC="img2.gif" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/img2.gif">
empirically. The estimate <IMG WIDTH=39 HEIGHT=28 ALIGN=MIDDLE ALT="tex2html_wrap_inline2000" SRC="img32.gif" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/img32.gif"> of the inverse obtained by the
rescaling of a finite sample to values drawn from a Gaussian distribution is
expected to be consistent but it is not exact for finite <I>N</I>. The sampling
fluctuations of <IMG WIDTH=161 HEIGHT=28 ALIGN=MIDDLE ALT="tex2html_wrap_inline2004" SRC="img33.gif" tppabs="http://www.mpipks-dresden.mpg.de/~tisean/TISEAN_2.0/docs/surropaper/img33.gif"> will be essentially
independent of <I>n</I> and thus spectrally white. Consequently, Gaussian scaling
amounts to adding a white component to the spectrum, which therefore tends to
become flatter under the procedure. Since such a bias can lead to spurious
results, surrogates have to be refined before a test can be performed.
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<P><ADDRESS>
<I>Thomas Schreiber <BR>
Mon Aug 30 17:31:48 CEST 1999</I>
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