CLT for single functional index quantile regression under dependence structure
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26. Aug. 2021
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Online veröffentlicht: 26. Aug. 2021
Seitenbereich: 45 - 77
Eingereicht: 31. Aug. 2020
DOI: https://doi.org/10.2478/ausm-2021-0003
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© 2021 Nadia Kadiri et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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.