vix.ing · top · new · best · stats · spec

Kernel-smoothed conditional quantiles of randomly censored functional stationary ergodic data

2013/04/15 by Mohamed Chaouch, Chaouch, Mohamed, Salah Khardani +1
Computer Science · Engineering · Environmental Science · Mathematics · #Bayesian Methods and Mixture Models #Control Systems and Identification #FOS: Computer and information sciences #FOS: Mathematics #Hydrology and Drought Analysis #Methodology (stat.ME) #Statistical Methods and Inference #Statistics Theory (math.ST) #math.ST #stat.ME #stat.TH

paper · pdf · doi:10.48550/arxiv.1304.4304

26 pages, 4 figures. arXiv admin note: text overlap with arXiv:1211.2780 by other authors

openalex publication_date 2013/04/15 · arxiv created 2013/04/16 · arxiv updated 2013/04/17 · openalex created_date 2022/10/03 · openalex updated_date 2026/07/28

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.

Citations

Related