Communications for Statistical Applications and Methods

  • 제25권4호
  • /
  • Pages.431-442
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  • 2018
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Computational explosion in the frequency estimation of sinusoidal data

  • Zhang, Kaimeng (Department of Statistics, Chonnam National University) ;
  • Ng, Chi Tim (Department of Statistics, Chonnam National University) ;
  • Na, Myunghwan (Department of Statistics, Chonnam National University)
  • 투고 : 2018.04.20
  • 심사 : 2018.06.08
  • 발행 : 2018.07.31
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초록

This paper highlights the computational explosion issues in the autoregressive moving average approach of frequency estimation of sinusoidal data with a large sample size. A new algorithm is proposed to circumvent the computational explosion difficulty in the conditional least-square estimation method. Notice that sinusoidal pattern can be generated by a non-invertible non-stationary autoregressive moving average (ARMA) model. The computational explosion is shown to be closely related to the non-invertibility of the equivalent ARMA model. Simulation studies illustrate the computational explosion phenomenon and show that the proposed algorithm can efficiently overcome computational explosion difficulty. Real data example of sunspot number is provided to illustrate the application of the proposed algorithm to the time series data exhibiting sinusoidal pattern.

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참고문헌

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