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Nonparametric high resolution spectral estimation
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  • Published: June 1990

Nonparametric high resolution spectral estimation

  • R. Dahlhaus1 

Probability Theory and Related Fields volume 85, pages 147–180 (1990)Cite this article

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Summary

The uniform rate of convergence of the integrated relative mean square error over a (with the sample sizeT) increasing classI T of stationary processes is studied for several estimates of the spectral density. The classI T is chosen in a way such that estimates with a good uniform rate of convergence overI T may be termed ‘high resolution spectral estimates’. By using this criterion several effects are explained theoretically, for example the leakage effect. The advantages uf using data tapers are proved and the use of local and global bandwiths are studied. Furthermore, the behaviors of segment estimates are studied. Simulations are presented for the illustration of some effects.

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Author information

Authors and Affiliations

  1. Institut für Angewandte Mathematik, Universität Heidelberg, Im Neuenheimer Feld 294, D-6900, Heidelberg, Federal Republic of Germany

    R. Dahlhaus

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  1. R. Dahlhaus
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Additional information

This work has been supported by the Deutsche Forschungsgemeinschaft

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Cite this article

Dahlhaus, R. Nonparametric high resolution spectral estimation. Probab. Th. Rel. Fields 85, 147–180 (1990). https://doi.org/10.1007/BF01277980

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  • Received: 02 November 1988

  • Revised: 27 December 1989

  • Issue Date: June 1990

  • DOI: https://doi.org/10.1007/BF01277980

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Keywords

  • High Resolution
  • Stochastic Process
  • Stationary Process
  • Probability Theory
  • Spectral Density
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