Surrogate testing of linear feedback processes with non-Gaussian innovations

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dc.creatorNagarajan, Radhakrishnan
dc.date2005-10-19
dc.date2006-03-16
dc.date.accessioned2026-07-07T06:44:35Z
dc.date.available2026-07-07T06:44:35Z
dc.descriptionSurrogate 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.
dc.description18 Pages, 6 Figures, Appendix
dc.identifierhttps://arxiv.org/abs/cond-mat/0510517
dc.identifierhttp://arxiv.org/abs/cond-mat/0510517
dc.identifierPhysica A, 2005
dc.identifierdoi:10.1016/j.physa.2005.10.041
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/102821
dc.subjectStatistical Mechanics
dc.subjectChaotic Dynamics
dc.titleSurrogate testing of linear feedback processes with non-Gaussian innovations
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