A strong uniform convergence rate of a kernel conditional quantile estimator under random left-truncation and dependent data

2008/10/07 by Elias Ould‐Saïd, Elias Ould-Saïd, Djabrane Yahia +4
Mathematics · #FOS: Mathematics #Mathematical Approximation and Integration #Statistical Methods and Inference #Statistics Theory (math.ST) #math.ST #stat.TH

paper · pdf · doi:10.48550/arxiv.0810.1156

Submitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)

arxiv created 2008/10/07 · openalex publication_date 2008/10/07 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/03

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-Saïd and Poulin [16] in the iid case. The uniform strong convergence rate of the estimator under strong mixing hypothesis is obtained.

Citations

• Consistent Nonparametric Regression

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