A strong uniform convergence rate of a kernel conditional quantile estimator under random left-truncation and dependent data
Statistics Theory
2008-10-08 v1 Statistics Theory
Abstract
In this paper we study some asymptotic properties of the kernel conditional quantile estimator with randomly left-truncated data which exhibit some kind of dependence. We extend the result obtained by Lemdani, Ould-Sa\"id and Poulin [16] in the iid case. The uniform strong convergence rate of the estimator under strong mixing hypothesis is obtained.
Cite
@article{arxiv.0810.1156,
title = {A strong uniform convergence rate of a kernel conditional quantile estimator under random left-truncation and dependent data},
author = {Elias Ould-Saïd and Djabrane Yahia and Abdelhakim Necir},
journal= {arXiv preprint arXiv:0810.1156},
year = {2008}
}
Comments
Submitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)