Volume no :22, Issue no: 1, December (2019)

Journal of Statistics: Advances in Theory and Applications

KERNEL DENSITY ESTIMATION FOR LINEAR PROCESSES WITH DEPENDENT INNOVATIONS: ASYMPTOTIC NORMALITY

Author's: Kaouthar El Fassi and Lahcen Douge
Pages: [1] - [19]
Received Date: July 29, 2019; Revised September 30, 2019
Submitted by:
DOI: http://dx.doi.org/10.18642/jsata_7100122082

Abstract

In this paper, we study the kernel estimate of the density function of linear processes with dependent innovations. The asymptotic normality is shown under general conditions and some conditions on the decay of the weak dependence coefficients. Some numerical results based on simulations are also presented and discussed.

Keywords

linear process, dependence, kernel estimate.