SOME PROPERTIES OF AUTOREGRESSIVE ESTIMATES FOR PROCESSES WITH MIXED SPECTRA

M. S. Mackisack, D. S. Poskitt

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)

Abstract

Abstract. A formal justification for the use of the method of autoregressive spectral estimation for time series consisting of a sinusoidal signal in additive noise is given in this paper. The analytical properties of the autoregressive approximation to the generalized spectral density of the process are presented, and the operational characteristics of the statistical estimation procedure are discussed. In particular, strong convergence of the autoregressive parameters and the autoregressive transfer function approximation is shown.

Original languageEnglish
Pages (from-to)325-337
Number of pages13
JournalJournal of Time Series Analysis
Volume11
Issue number4
DOIs
Publication statusPublished - 1 Jan 1990
Externally publishedYes

Keywords

  • approximation
  • Autoregression
  • convergence
  • estimation
  • frequency
  • mixed spectra
  • spectrum
  • transfer function

Cite this

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abstract = "Abstract. A formal justification for the use of the method of autoregressive spectral estimation for time series consisting of a sinusoidal signal in additive noise is given in this paper. The analytical properties of the autoregressive approximation to the generalized spectral density of the process are presented, and the operational characteristics of the statistical estimation procedure are discussed. In particular, strong convergence of the autoregressive parameters and the autoregressive transfer function approximation is shown.",
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SOME PROPERTIES OF AUTOREGRESSIVE ESTIMATES FOR PROCESSES WITH MIXED SPECTRA. / Mackisack, M. S.; Poskitt, D. S.

In: Journal of Time Series Analysis, Vol. 11, No. 4, 01.01.1990, p. 325-337.

Research output: Contribution to journalArticleResearchpeer-review

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N2 - Abstract. A formal justification for the use of the method of autoregressive spectral estimation for time series consisting of a sinusoidal signal in additive noise is given in this paper. The analytical properties of the autoregressive approximation to the generalized spectral density of the process are presented, and the operational characteristics of the statistical estimation procedure are discussed. In particular, strong convergence of the autoregressive parameters and the autoregressive transfer function approximation is shown.

AB - Abstract. A formal justification for the use of the method of autoregressive spectral estimation for time series consisting of a sinusoidal signal in additive noise is given in this paper. The analytical properties of the autoregressive approximation to the generalized spectral density of the process are presented, and the operational characteristics of the statistical estimation procedure are discussed. In particular, strong convergence of the autoregressive parameters and the autoregressive transfer function approximation is shown.

KW - approximation

KW - Autoregression

KW - convergence

KW - estimation

KW - frequency

KW - mixed spectra

KW - spectrum

KW - transfer function

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