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CLT for single functional index quantile regression under dependence structure

Open Access
|Aug 2021

Abstract

In this paper, we investigate the asymptotic properties of a nonparametric conditional quantile estimation in the single functional index model for dependent functional data and censored at random responses are observed. First of all, we establish asymptotic properties for a conditional distribution estimator from which we derive an central limit theorem (CLT) of the conditional quantile estimator. Simulation study is also presented to illustrate the validity and finite sample performance of the considered estimator. Finally, the estimation of the functional index via the pseudo-maximum likelihood method is discussed, but not tackled.

Language: English
Page range: 45 - 77
Submitted on: Aug 31, 2020
Published on: Aug 26, 2021
Published by: Sapientia Hungarian University of Transylvania
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2021 Nadia Kadiri, Abbes Rabhi, Salah Khardani, Fatima Akkal, published by Sapientia Hungarian University of Transylvania
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.