Physics – Data Analysis – Statistics and Probability
Scientific paper
1999-05-07
Physics
Data Analysis, Statistics and Probability
14 pages, 15 figures, submitted to Physical Review E
Scientific paper
10.1103/PhysRevE.60.2808
The schemes for the generation of surrogate data in order to test the null hypothesis of linear stochastic process undergoing nonlinear static transform are investigated as to their consistency in representing the null hypothesis. In particular, we pinpoint some important caveats of the prominent algorithm of amplitude adjusted Fourier transform surrogates (AAFT) and compare it to the iterated AAFT (IAAFT), which is more consistent in representing the null hypothesis. It turns out that in many applications with real data the inferences of nonlinearity after marginal rejection of the null hypothesis were premature and have to be re-investigated taken into account the inaccuracies in the AAFT algorithm, mainly concerning the mismatching of the linear correlations. In order to deal with such inaccuracies we propose the use of linear together with nonlinear polynomials as discriminating statistics. The application of this setup to some well-known real data sets cautions against the use of the AAFT algorithm.
No associations
LandOfFree
Test your surrogate data before you test for nonlinearity does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Test your surrogate data before you test for nonlinearity, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Test your surrogate data before you test for nonlinearity will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-599357