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Fitting autoregression with regularly missed observations

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Abstract

The effect of regularly missed observations on the estimation of parameters of an autoregressive (AR) process is investigated by using the frequency domain method. For first order AR processes, numerical results are shown to see a behavior of variances of the estimate due to the missed observations. In some cases, we can positively utilize the concept of missed observations to decrease the variances if the number of observations is fixed but time instants at with the observations are made can be changed.

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References

  1. Akaike, H. (1970). Statistical predictor identification,Ann. Inst. Statist. Math.,22, 203–217.

    Article  MathSciNet  Google Scholar 

  2. Alekseev, V. G. and Savitsky, Yu. A. (1973). On spectral analysis of Gaussian random processes with missing observations.Proc. Inform. Transmiss.,9, 66–72.

    Google Scholar 

  3. Bloomfield, P. (1970). Spectral analysis with randomly missing observations,J. R. Statist. Soc., B,32, 369–380.

    MathSciNet  MATH  Google Scholar 

  4. Brillinger, D. R. (1975).Time Series: Data Analysis and Theory, Holt, Rinehart, and Winston, Inc., New York.

    MATH  Google Scholar 

  5. Jones, R. H. (1962). Spectral analysis with regularly missed observations,Ann. Math. Statist.,33, 455–461.

    Article  MathSciNet  Google Scholar 

  6. Jones, R. H. (1971). Spectrum estimation with missing observations,Ann. Inst. Statist. Math.,23, 387–398.

    Article  MathSciNet  Google Scholar 

  7. Neave, H. R. (1970). Extending the frequency range of spectrum estimate by the use of two data recorders,Technometrics,12, 877–890.

    Article  Google Scholar 

  8. Parzen, E. (1963). On spectral analysis with missing observations and amplitude modulation,Sankhyã, A,25, 383–392.

    MathSciNet  MATH  Google Scholar 

  9. Sakai, H., Soeda, T. and Tokumaru, H. (1979). On the relation between fitting autoregression and periodogram with applications,Ann. Statist.,7, 96–107.

    Article  MathSciNet  Google Scholar 

  10. Scheinok, P. A. (1965). Spectral analysis with randomly missed observations: the binomial case,Ann. Math. Statist.,36, 971–977.

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Hideaki Sakai

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  1. Hideaki Sakai
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Sakai, H. Fitting autoregression with regularly missed observations. Ann Inst Stat Math 32, 393–400 (1980). https://doi.org/10.1007/BF02480344

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  • DOI: https://doi.org/10.1007/BF02480344

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