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

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dc.creatorOuld-Saïd, Elias
dc.creatorYahia, Djabrane
dc.creatorNecir, Abdelhakim
dc.date2008-10-07
dc.date.accessioned2026-07-07T10:08:05Z
dc.date.available2026-07-07T10:08:05Z
dc.descriptionIn 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.
dc.descriptionSubmitted to the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0810.1156
dc.identifierhttp://arxiv.org/abs/0810.1156
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/170872
dc.subjectStatistics Theory
dc.titleA strong uniform convergence rate of a kernel conditional quantile estimator under random left-truncation and dependent data
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