NONPARAMETRIC ESTIMATION FORA NONLINEAR STABLE SAMPLE PROCESS

Abstract

Nonparametric density estimation is a useful tool for examining the structure of time series. In this paper, we will adopt a kernel method to estimate unknown density function for a nonlinear stable sample process based on the Á phi-mixing process. Under mild conditions, the uniformly weak and strong consistency and the convergence rates of the estimator are obtained.