Single Functional Index Quantile Regression for Independent Functional Data Under Right-Censoring

Hamri Mohamed Mehdi (), Mekki Sanaà Dounya (), Rabhi Abbes () and Kadiri Nadia ()
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Hamri Mohamed Mehdi: LABRI, ESI, Sidi Bel Abbes, University Djillali Liabes of Sidi Bel Abbes, Sidi Bel Abbes, Algeria
Mekki Sanaà Dounya: Department of Mathematics and Computer Science, University Center Salhi Ahmed of Naâma, Sidi Bel Abbes, Algeria
Rabhi Abbes: University Djillali Liabes of Sidi Bel Abbes, Sidi Bel Abbes, Algeria
Kadiri Nadia: University Djillali Liabes of Sidi Bel Abbes, Sidi Bel Abbes, Algeria

Econometrics. Advances in Applied Data Analysis, 2022, vol. 26, issue 1, 31-62

Abstract: The main objective of this paper was to estimate non-parametrically the quantiles of a conditional distribution based on the single-index model in the censorship model when the sample is considered as independent and identically distributed (i.i.d.) random variables. First of all, a kernel type estimator for the conditional cumulative distribution function (cond-cdf) is introduced. Then the paper gives an estimation of the quantiles by inverting this estimated cond-cdf, the asymptotic properties are stated when the observations are linked with a single-index structure. Finally, a simulation study was carried out to evaluate the performance of this estimate.

Keywords: censored data; functional data; kernel estimator; normality; non-parametric estimation; small ball probability (search for similar items in EconPapers)
JEL-codes: C13 C14 C15 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:eaiada:v:26:y:2022:i:1:p:31-62:n:1

DOI: 10.15611/eada.2022.1.03

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