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
Source: Electron. J. Statist. Volume 3 (2009), 426-445.
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.
Primary Subjects: 62G05, 62G20
Keywords: Kernel estimator; quantile function; rate of convergence; strong mixing; strong uniform consistency; truncated data
Full-text: Open access
Links and Identifiers
Permanent link to this document: http://projecteuclid.org/euclid.ejs/1243343760
Digital Object Identifier: doi:10.1214/08-EJS306
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