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Kernel conditional quantile estimator for functional regressors under a twice censorship model

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  • Ranya Boustila
  • Sarra Leulmi
  • Farid Leulmi

Abstract

This article is concerned with the kernel non parametric conditional quantile estimator of a twice censored scalar response random variable, given a functional random covariate. First, we investigate the almost complete convergence of the kernel conditional distribution estimator under the concentration properties of small ball probability functions. Then, we apply this result to establish the almost complete convergence of conditional quantile estimator. To lend supplementary support to our theoretical results, we conduct a simulation study to illustrate the performance and the accuracy of our proposed estimator.

Suggested Citation

  • Ranya Boustila & Sarra Leulmi & Farid Leulmi, 2025. "Kernel conditional quantile estimator for functional regressors under a twice censorship model," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(21), pages 6997-7010, November.
    • Handle: RePEc:taf:lstaxx:v:54:y:2025:i:21:p:6997-7010
    DOI: 10.1080/03610926.2025.2472787
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