<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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.</p>
</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.

CLT for single functional index quantile regression under dependence structure

University Djillali LIABES of Sidi Bel Abbes

Acta Universitatis Sapientiae, Mathematica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"CLT for single functional index quantile regression under dependence structure"}

In this paper, we investigate the asymptotic properties of a nonparametric conditional quantile estimation in the single functional index model for dependent...

CLT for single functional index quantile regression under dependence structure

Online veröffentlicht: 26. Aug. 2021

Seitenbereich: 45 - 77

Eingereicht: 31. Aug. 2020

DOI: https://doi.org/10.2478/ausm-2021-0003

Schlüsselwörter
conditional quantile, censored data, functional random variable, Kernel estimator, nonparametric estimation, probabilities of small balls, strong mixing processes, single index model

© 2021 Nadia Kadiri et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

CLT for single functional index quantile regression under dependence structure

Online veröffentlicht: 26. Aug. 2021

Seitenbereich: 45 - 77

Eingereicht: 31. Aug. 2020

DOI: https://doi.org/10.2478/ausm-2021-0003

Schlüsselwörterconditional quantile, censored data, functional random variable, Kernel estimator, nonparametric estimation, probabilities of small balls, strong mixing processes, single index model

© 2021 Nadia Kadiri et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
conditional quantile, censored data, functional random variable, Kernel estimator, nonparametric estimation, probabilities of small balls, strong mixing processes, single index model