Nonparametric estimation of a regression function using the gamma kernel method in ergodic processes
Statistics Theory
2016-10-18 v2 Statistics Theory
Abstract
In this paper we consider the nonparametric estimation of density and regression functions with non-negative support using a gamma kernel procedure introduced by Chen (2000). Strong uniform consistency and asymptotic normality of the corresponding estimators are established under a general ergodic assumption on the data generation process. Our results generalize those of Shi and Song (2016), obtained in the classic i.i.d. framework, and the works of Bouezmarni and Rombouts (2008, 2010b) and Gospodinov and Hirukawa (2007) for mixing time series.
Cite
@article{arxiv.1605.07520,
title = {Nonparametric estimation of a regression function using the gamma kernel method in ergodic processes},
author = {A. C. Rosa and M. E. Nogueira},
journal= {arXiv preprint arXiv:1605.07520},
year = {2016}
}
Comments
29 pages