Electronic Journal of Statistics

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

Elias Ould-Saïd, Djabrane Yahia, and Abdelhakim Necir

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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.

Article information

Electron. J. Statist., Volume 3 (2009), 426-445.

First available in Project Euclid: 26 May 2009

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Zentralblatt MATH identifier

Primary: 62G05: Estimation 62G20: Asymptotic properties

Kernel estimator quantile function rate of convergence strong mixing strong uniform consistency truncated data


Ould-Saïd, Elias; Yahia, Djabrane; Necir, Abdelhakim. A strong uniform convergence rate of a kernel conditional quantile estimator under random left-truncation and dependent data. Electron. J. Statist. 3 (2009), 426--445. doi:10.1214/08-EJS306. https://projecteuclid.org/euclid.ejs/1243343760

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