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- W2000419203 abstract "We study the multivariate surrogate data method of Prichard and Theiler [D. Prichard and J. Theiler, Generating surrogate data for time series with several simultaneously measured variables, Phys. Rev. Lett. 73 (1994), pp. 951–954.]. This approach is an extension of the univariate surrogate data method introduced in Theiler et al. [J. Theiler, S. Eubank, A. Longtin, B. Galdrikan, and J.D. Farmer, Testing for nonlinearity in time series: The method of surrogate data, Physica D 58 (1992), pp. 77–94.]. Surrogate data is a resampling method for linear time series that is based on phase-randomized Fourier transforms. Theoretical properties of the multivariate method are derived. We show that the conditional distribution of surrogates is asymptotically normal. Furthermore, it is shown that surrogate data is the only method to construct tests that have constant level on the hypothesis of multivariate stationary Gaussian circular processes. We argue that this result approximately holds for non-circular processes ..." @default.
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- W2000419203 date "2008-06-01" @default.
- W2000419203 modified "2023-09-26" @default.
- W2000419203 title "Some theoretical properties of phase-randomized multivariate surrogates" @default.
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- W2000419203 doi "https://doi.org/10.1080/02331880701736572" @default.
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