Surrogate data for non-stationary signals

Andreas Schmitz; Thomas Schreiber

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arXiv · chao-dyn/9904023

Surrogate data for non-stationary signals

Andreas Schmitz·Thomas Schreiber

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Abstract

Standard tests for nonlinearity reject the null hypothesis of a Gaussian linear process whenever the data is non-stationary. Thus, they are not appropriate to distinguish nonlinearity from non-stationarity. We address the problem of generating proper surrogate data corresponding to the null hypothesis of an ARMA process with slowly varying coefficients.

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Andreas Schmitz, Thomas Schreiber. 1999-04-13. Surrogate data for non-stationary signals. https://arxiv.org/abs/chao-dyn/9904023

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