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Nonparametric discrimination of time series data

Author

Listed:
  • Rahim Chinipardaz
  • Trevor Cox

Abstract

A normality assumption is usually made for the discrimination between two stationary time series processes. A nonparametric approach is desirable whenever there is doubt concerning the validity of this normality assumption. In this paper a nonparametric approach is suggested based on kernel density estimation firstly on (p+1) sample autocorrelations and secondly on (p+1) consecutive observations. A numerical comparison is made between Fisher’s linear discrimination based on sample autocorrelations and kernel density discrimination for AR and MA processes with and without Gaussian noise. The methods are applied to some seismological data. Copyright Springer-Verlag 2004

Suggested Citation

  • Rahim Chinipardaz & Trevor Cox, 2004. "Nonparametric discrimination of time series data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 59(1), pages 13-20, February.
    • Handle: RePEc:spr:metrik:v:59:y:2004:i:1:p:13-20
    DOI: 10.1007/s001840300267
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    Cited by:

    1. Maharaj, Elizabeth A. & Alonso, Andres M., 2007. "Discrimination of locally stationary time series using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 879-895, October.
    2. Saavedra Pedro & Santana-del-Pino Angelo & Hernández-Flores Carmen N. & Artiles-Romero Juan & González-Henríquez Juan J., 2011. "Classification of Stationary Signals with Mixed Spectrum," The International Journal of Biostatistics, De Gruyter, vol. 7(1), pages 1-17, January.

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