KERNEL DISCRIMINATION OF TIME SERIES DATA
Rahim Chiniparadaz · RePEc: Research Papers in Economics · 2000
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.}