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

Author

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  • Navarro-Moreno, Jesús
  • Moreno-Kaiser, Javier
  • Fernández-Alcalá, Rosa María
  • Ruiz-Molina, Juan Carlos

Abstract

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.

Suggested Citation

  • Navarro-Moreno, Jesús & Moreno-Kaiser, Javier & Fernández-Alcalá, Rosa María & Ruiz-Molina, Juan Carlos, 2013. "Widely linear prediction for transfer function models based on the infinite past," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 139-146.
    • 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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    References listed on IDEAS

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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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    Cited by:

    1. M. Azimmohseni & M. Khalafi & M. Kordkatuli, 2019. "Time series analysis of covariance based on linear transfer function models," Statistical Inference for Stochastic Processes, Springer, vol. 22(1), pages 1-16, April.

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