The power of surrogate data testing with respect to non-stationarity
arXiv:chao-dyn/9807039 · doi:10.1103/PhysRevE.58.5153
Abstract
Surrogate data testing is a method frequently applied to evaluate the results of nonlinear time series analysis. Since the null hypothesis tested against is a linear, gaussian, stationary stochastic process a positive outcome may not only result from an underlying nonlinear or even chaotic system, but also from e.g. a non-stationary linear one. We investigate the power of the test against non-stationarity.
4 pages, 4 figures, to appear in PRE