Kernel-smoothed conditional quantiles of randomly censored functional stationary ergodic data
Year: 2013
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Chaouch, Mohamed, Khardani, Salah
Institutions: University of Reading, Université Lille Nord de France
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2098839452