On the strong uniform consistency for relative error of the regression function estimator for censoring times series model
Statistics Theory
2019-10-07 v1 Applications
Computation
Statistics Theory
Abstract
Consider a random vector (X, T), where X is d-dimensional and T is one-dimensional. We suppose that the random variable T is subject to random right censoring and satisfies the -mixing property. The aim of this paper is to study the behavior of the kernel estimator of the relative error regression and to establish its uniform almost sure consistency with rate. Furthermore, we have highlighted the covariance term which measures the dependency. The simulation study shows that the proposed estimator performs well for a finite sample size in different cases.
Keywords
Cite
@article{arxiv.1910.01964,
title = {On the strong uniform consistency for relative error of the regression function estimator for censoring times series model},
author = {Bouhadjera Feriel and Elias Ould Said},
journal= {arXiv preprint arXiv:1910.01964},
year = {2019}
}