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Widely linear prediction for transfer function models based on the infinite past

Listed author(s):
  • Navarro-Moreno, Jesús
  • Moreno-Kaiser, Javier
  • Fernández-Alcalá, Rosa María
  • Ruiz-Molina, Juan Carlos
Registered author(s):

    The problem of widely linear (WL) prediction for both WL ARMA models and WL transfer function models on the basis of infinite past information is studied. A recursive algorithm to obtain a suboptimum predictor for WL ARMA systems is first given. Then this algorithm is used to develop another recursive algorithm which performs WL prediction for transfer function models. The suggested solutions become an alternative to the WL prediction based on a finite number of observations provided the size of the time series is sufficiently large.

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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 58 (2013)
    Issue (Month): C ()
    Pages: 139-146

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    Handle: RePEc:eee:csdana:v:58:y:2013:i:c:p:139-146
    DOI: 10.1016/j.csda.2010.11.020
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    1. Kundu, Debasis, 1994. "Estimating the parameters of complex-valued exponential signals," Computational Statistics & Data Analysis, Elsevier, vol. 18(5), pages 525-534, December.
    2. Ollila, Esa & Oja, Hannu & Koivunen, Visa, 2008. "Complex-valued ICA based on a pair of generalized covariance matrices," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3789-3805, March.
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