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Kernel Discrimination Of Time Series Data

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  • Rahim Chiniparadaz

    (Shahid Chamran University, Ahwaz, Iran.)

Abstract

A normality assumption is usually made for the discrimination between two stationary time series processes. A kernel 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 secondly on ( ple autocorrelations and kernel density discrimination for AR and MA processes with and without Gaussian noise. The methods are applied to some seismological data.}

Suggested Citation

  • Rahim Chiniparadaz, 2000. "Kernel Discrimination Of Time Series Data," Computing in Economics and Finance 2000 167, Society for Computational Economics.
    • Handle: RePEc:sce:scecf0:167
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