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Single functional index quantile regression under general dependence structure

Author

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  • Mohamed Chaouch
  • Amina Angelika Bouchentouf
  • Aboubacar Traore
  • Abbes Rabhi

Abstract

The main purpose of this paper is to estimate, semi-parametrically, the quantiles of a conditional distribution when the response is a real-valued random variable subject to a right-censorship phenomenon and the predictor takes values in an infinite dimensional space. We assume that the explanatory and the response variables are linked through a single-index structure. First, we introduce a kernel-type estimator of the conditional quantile when the data are supposed to be selected from an underlying stationary and ergodic process. Then, under some general conditions, the uniform almost-complete convergence rate as well as the asymptotic distribution of the estimator are established. A numerical study, including simulated and real data application, is performed to illustrate the validity and the finite-sample performance of the considered estimator.

Suggested Citation

  • Mohamed Chaouch & Amina Angelika Bouchentouf & Aboubacar Traore & Abbes Rabhi, 2020. "Single functional index quantile regression under general dependence structure," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(3), pages 725-755, July.
    • Handle: RePEc:taf:gnstxx:v:32:y:2020:i:3:p:725-755
    DOI: 10.1080/10485252.2020.1797021
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    Cited by:

    1. Bouzebda, Salim & Chaouch, Mohamed, 2022. "Uniform limit theorems for a class of conditional Z-estimators when covariates are functions," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    2. Hamri Mohamed Mehdi & Mekki Sanaà Dounya & Rabhi Abbes & Kadiri Nadia, 2022. "Single Functional Index Quantile Regression for Independent Functional Data Under Right-Censoring," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 26(1), pages 31-62, March.
    3. Ufuk Beyaztas & Han Lin Shang & Aylin Alin, 2022. "Function-on-Function Partial Quantile Regression," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(1), pages 149-174, March.

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