Abstract
In this paper we consider the estimation of spectrum and bispectrum of a stationary time series. The usefulness of bispectrum to detect the periodicities of signals when the signals are corrupted by noise is pointed out. We show that spectral density function calculated from a fitted bilinear time series model can be a useful alternative when the time series is nonlinear. We illustrate the estimation methods with many simulated and one real data set. The real data is concerned with the magnetic reversals.
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Subba Rao, T. (1988). Spectral and bispectral methods for the analysis of nonlinear (non Gaussian) time series signals. In: Mohler, R.R. (eds) Nonlinear Time Series and Signal Processing. Lecture Notes in Control and Information Sciences, vol 106. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0044273
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DOI: https://doi.org/10.1007/BFb0044273
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