SLEX Analysis of Multivariate Nonstationary Time Series

By Hernando C. Ombao, R. von Sachs, Wensheng Guo. 2005

219 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e2f98991ad150f01c86ec6a85e510bfe1f45caae

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In Management Science and Operations Research

In Signal Processing

Citations in the corpus

Multiple‐change‐point detection for high dimensional time series via sparsified binary segmentation (2015, methodology): “Another example can be found in neuroscience, where electroencephalograms (EEG) recorded at multiple channels exhibit nonstationarity and high correlations as well as being massive in volume (Ombao et al. 2005).”
Editorial: Special issue on time series analysis in the biological sciences (2012, methodology): “…have been proposed, the locally stationary Fourier-based model of Priestley (1965) and Dahlhaus (1997), the locally stationary wavelets model in Nason et al. (2000), the SLEX (smooth localized Fourier) model in Ombao et al. (2005), and a local spline approach taken in Rosen et al. (2009).”
Finite sample change point inference and identification for high‐dimensional mean vectors (2017, methodology): “Change point testing and estimation have a long history [45, 46, 41, 16, 31, 49, 10, 20, 33, 6, 23, 13, 37, 29, 27, 22, 24, 40, 3, 4, 7, 51, 30] and there are two major lines of change point tests in literature: sequential tests and fixed sample size tests [34].”
Uniform change point tests in high dimension (2015, methodology): “Using a threshold-aggregation approach, [15] study the detection of global changes (see also [27, 41]), whereas in [34], the topic of possible gain or loss in power in higher dimension is discussed.”
Oil and the macroeconomy: using wavelets to analyze old issues (2011, methodology): “On this regard, we see multivariate spectral analysis using Hilbert wavelet pairs – see Crowley et al. (2006) – and SLEX (smooth localized complex exponentials) Analysis of Multivariate Nonstationary Time Series – see Ombao et al. (2005) – as promising venues.”
A review on the applications of wavelet transform in hydrology time series analysis (2013, methodology): “Since hydrologic series usually show complex characteristics (Ahalpara et al., 2008; Ombao et al., 2005; Pinnegar and Mansinha, 2003), traditional spectral analysis methods (Wiener filtering, Kalman filtering, Fourier transform, etc.”