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Nonparametric discrimination of time series data

Lookup NU author(s): Dr Trevor Cox

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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. © Springer-Verlag 2004.

Publication metadata

Author(s): Chinipardaz R, Cox T

Publication type: Article

Publication status: Published

Journal: Metrika

Year: 2004

Volume: 59

Issue: 1

Pages: 13-20

Print publication date: 01/01/2004

ISSN (print): 0026-1335

ISSN (electronic): 1435-926X

Publisher: Springer

URL: http://dx.doi.org/10.1007/s001840300267

DOI: 10.1007/s001840300267

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