Surrogate testing of linear feedback processes with non-Gaussian innovations
arXiv:cond-mat/0510517 · doi:10.1016/j.physa.2005.10.041
Abstract
Surrogate testing is used widely to determine the nature of the process generating the given empirical sample. In the present study, the usefulness of phase-randomized surrogates, amplitude adjusted Fourier transform (AAFT) and iterated amplitude adjusted Fourier transform (IAAFT) surrogates on statistical inference of linearly correlated noise with non-Gaussian innovations and their static, invertible nonlinear transforms from their empirical samples is discussed. Existing surrogate testing procedures which retain the auto-correlation function in the surrogates may not be appropriate in the presence of non-Gaussian innovations.
18 Pages, 6 Figures, Appendix