Kernel-smoothed conditional quantiles of randomly censored functional stationary ergodic data
Abstract
This paper, investigates the conditional quantile estimation of a scalar random response and a functional random covariate (i.e. valued in some infinite-dimensional space) whenever {\it functional stationary ergodic data with random censorship} are considered. We introduce a kernel type estimator of the conditional quantile function. We establish the strong consistency with rate of this estimator as well as the asymptotic normality which induces a confidence interval that is usable in practice since it does not depend on any unknown quantity. An application to electricity peak demand interval prediction with censored smart meter data is carried out to show the performance of the proposed estimator.
Cite
@article{arxiv.1304.4304,
title = {Kernel-smoothed conditional quantiles of randomly censored functional stationary ergodic data},
author = {Mohamed Chaouch and Salah Khardani},
journal= {arXiv preprint arXiv:1304.4304},
year = {2013}
}
Comments
26 pages, 4 figures. arXiv admin note: text overlap with arXiv:1211.2780 by other authors