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Data filtering based multi-innovation extended gradient method for controlled autoregressive autoregressive moving average systems using the maximum likelihood principle

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  • Chen, Feiyan
  • Ding, Feng
  • Alsaedi, Ahmed
  • Hayat, Tasawar

Abstract

This paper combines the data filtering technique with the maximum likelihood principle for parameter estimation of controlled autoregressive ARMA (autoregressive moving average) systems. We use an estimated noise transfer function to filter the input–output data and derive a filtering based maximum likelihood multi-innovation extended gradient algorithm to estimate the parameters of the systems by replacing the unmeasurable variables in the information vectors with their estimates. A maximum likelihood generalized extended gradient algorithm is given for comparison. A numerical simulation is given to support the developed methods.

Suggested Citation

  • Chen, Feiyan & Ding, Feng & Alsaedi, Ahmed & Hayat, Tasawar, 2017. "Data filtering based multi-innovation extended gradient method for controlled autoregressive autoregressive moving average systems using the maximum likelihood principle," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 132(C), pages 53-67.
    • Handle: RePEc:eee:matcom:v:132:y:2017:i:c:p:53-67
    DOI: 10.1016/j.matcom.2016.06.006
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    References listed on IDEAS

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    1. Andrews, Donald W.K. & Cheng, Xu, 2013. "Maximum likelihood estimation and uniform inference with sporadic identification failure," Journal of Econometrics, Elsevier, vol. 173(1), pages 36-56.
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    3. Feng, Xinlong & He, Guoliang & Abdurishit,, 2008. "Estimation of parameters of the Makeham distribution using the least squares method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 77(1), pages 34-44.
    4. Tse, Y.K. & Anh, V.V. & Tieng, Q., 2002. "Maximum likelihood estimation of the fractional differencing parameter in an ARFIMA model using wavelets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 59(1), pages 153-161.
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