• DocumentCode
    1591237
  • Title

    Facts and fiction in spectral analysis

  • Author

    Broersen, P.M.T.

  • Author_Institution
    Dept. of Appl. Phys., Delft Univ. of Technol., Netherlands
  • Volume
    2
  • fYear
    1998
  • Firstpage
    1325
  • Abstract
    This analysis is limited to the spectral density of unknown stationary stochastic processes. The main estimation methods are parametric with time series, or non-parametric with a Fourier transform of the data. A single time series model is chosen automatically from the three selected models: the best autoregressive, AR, the best moving average, MA and the best combined ARMA. The accuracy of the spectrum, computed from this single ARMA time series model, is compared with the accuracy of windowed periodogram estimates. The time series model generally gives a spectrum that is better than the best periodogram. It is a fact that a single time series model can be selected automatically for unknown statistical data. It is fiction to believe that objective choices between windowed periodograms can be made
  • Keywords
    Fourier transforms; autoregressive moving average processes; estimation theory; spectral analysis; time series; ARMA; Fourier transform; autoregressive; moving average; nonparametric estimation methods; parametric estimation methods; spectral analysis; spectral density; time series; unknown stationary stochastic processes; unknown statistical data; windowed periodogram estimates; Books; Electronic switching systems; Fourier transforms; Physics; Signal processing; Spectral analysis; Stochastic processes; Time series analysis; White noise; World Wide Web;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Instrumentation and Measurement Technology Conference, 1998. IMTC/98. Conference Proceedings. IEEE
  • Conference_Location
    St. Paul, MN
  • ISSN
    1091-5281
  • Print_ISBN
    0-7803-4797-8
  • Type

    conf

  • DOI
    10.1109/IMTC.1998.676966
  • Filename
    676966