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Local linear estimate of the point at high risk: Spatial functional data case

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  • M. Abeidallah
  • B. Mechab
  • T. Merouan

Abstract

The purpose of the present paper is to investigate by the local linear method a nonparametric estimator of the point at high risk of scalar response variable given a functional variable when the observations are spatially dependent. The main goal is to establish the almost complete convergence with rate of this estimator under some general conditions. A practical example on the climatological data shows the usefulness of our theoretical study.

Suggested Citation

  • M. Abeidallah & B. Mechab & T. Merouan, 2020. "Local linear estimate of the point at high risk: Spatial functional data case," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(11), pages 2561-2584, June.
    • Handle: RePEc:taf:lstaxx:v:49:y:2020:i:11:p:2561-2584
    DOI: 10.1080/03610926.2019.1580735
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    Cited by:

    1. Yao, Binhong & Li, Peixing, 2023. "Covariance estimation error of incomplete functional data under RKHS framework," Applied Mathematics and Computation, Elsevier, vol. 443(C).
    2. Hengzhen Huang & Guangni Mo & Haiou Li & Hong-Bin Fang, 2022. "Representation Theorem and Functional CLT for RKHS-Based Function-on-Function Regressions," Mathematics, MDPI, vol. 10(14), pages 1-23, July.

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