Functional data analysis: local linear estimation of the $$L_1$$ L 1 -conditional quantiles

Fahimah A. Al-Awadhi (), Zoulikha Kaid (), Ali Laksaci (), Idir Ouassou () and Mustapha Rachdi ()
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Fahimah A. Al-Awadhi: Kuwait University
Zoulikha Kaid: King Khalid University
Ali Laksaci: King Khalid University
Idir Ouassou: Université Cadi Ayyad
Mustapha Rachdi: University Grenoble Alpes

Statistical Methods & Applications, 2019, vol. 28, issue 2, No 2, 217-240

Abstract: Abstract We consider a new estimator of the quantile function of a scalar response variable given a functional random variable. This new estimator is based on the $$L_1$$ L 1 approach. Under standard assumptions, we prove the almost-complete consistency as well as the asymptotic normality of this estimator. This new approach is also illustrated through some simulated data and its superiority, compared to the classical method, has been proved for practical purposes.

Keywords: Functional data analysis (FDA); Small ball probability; Local linear method (LLM); Conditional quantiles; Asymptotic normality; Almost-complete (a.co.) convergence; 62G08; 62G10; 62G35; 62G07; 62G32; 62G30; Secondary 62H12 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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DOI: 10.1007/s10260-018-00447-5

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