期刊
PHYSICA D-NONLINEAR PHENOMENA
卷 225, 期 2, 页码 219-228出版社
ELSEVIER
DOI: 10.1016/j.physd.2006.10.012
关键词
surrogate data; wavelet transform; constrained realisations; hypothesis testing
Hypothesis testing based on surrogate data has emerged as a popular way to test the null hypothesis that a signal is a realisation of a linear Gaussian, stochastic process. If these surrogates are constrained to the values and power spectrum of the original data there is no need to formulate a pivotal test statistic. In this paper a method is presented for generating constrained surrogates using a wavelet transform, introducing a threshold above which wavelet detail coefficients are pinned to their original values. Such surrogates avoid problems of nonstationarity for pseudo-periodic data and appear to be more robust than conventional approaches for situations where period modulation is corrupting a Gaussian stochastic process. When used for generating ensemble realisations of a process, the approach used here avoids some of the difficulties of methods based on simple randomisation of wavelet coefficients. (c) 2006 Elsevier B.V. All rights reserved.
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