Robust and Nonlinear Time Series Analysis

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Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense statio...
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Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense statio...
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  • Formats: pdf
  • ISBN: 9781461578215
  • Publication Date: 6 Dec 2012
  • Publisher: Springer New York
  • Product language: English
  • Drm Setting: DRM