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Spectral Ratio Discriminants And Information Theory

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  • G. R. Dargahi‐Noubary
  • P. J. Laycock

Abstract

. We examine the problem of discriminating in the frequency domain between zero mean stationary Gaussian processes. Use of the Kullback‐Liebler information measure enables the selection of the important frequency bands, and a special case relevant to seismic records leads to a justification for the use of spectral ratios in discrimination. Methods are given for combining the ratios when there are more than two relevant bands, and for estimating the error rates when using spectral ratios. The results are extended to a special class of non‐stationary models.

Suggested Citation

  • G. R. Dargahi‐Noubary & P. J. Laycock, 1981. "Spectral Ratio Discriminants And Information Theory," Journal of Time Series Analysis, Wiley Blackwell, vol. 2(2), pages 71-86, March.
    • Handle: RePEc:bla:jtsera:v:2:y:1981:i:2:p:71-86
    DOI: 10.1111/j.1467-9892.1981.tb00313.x
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    Cited by:

    1. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.
    2. Galeano, Pedro & Peña, Daniel, 2001. "Multivariate analysis in vector time series," DES - Working Papers. Statistics and Econometrics. WS ws012415, Universidad Carlos III de Madrid. Departamento de Estadística.
    3. Dette, Holger & Paparoditis, Efstathios, 2008. "Bootstrapping frequency domain tests in multivariate time series with an application to comparing spectral densities," Technical Reports 2008,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

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