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Article Dans Une Revue Statistics & Risk Modeling with Applications in Finance and Insurance Année : 2021

The functional k NN estimator of the conditional expectile: Uniform consistency in number of neighbors

Ibrahim Almanjahie
Zouaoui Chikr Elmezouar
Ali Laksaci

Résumé

Abstract The main purpose of the present paper is to investigate the problem of the nonparametric estimation of the expectile regression in which the response variable is scalar while the covariate is a random function. More precisely, an estimator is constructed by using the k Nearest Neighbor procedures ( k NN). The main contribution of this study is the establishment of the Uniform consistency in Number of Neighbors (UNN) of the constructed estimator. The usefulness of our result for the smoothing parameter automatic selection is discussed. Short simulation results show that the finite sample performance of the proposed estimator is satisfactory in moderate sample sizes. We finally examine the implementation of this model in practice with a real data in financial risk analysis.
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Dates et versions

hal-03375532 , version 1 (12-10-2021)

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Citer

Salim Bouzebda, Ibrahim Almanjahie, Zouaoui Chikr Elmezouar, Ali Laksaci. The functional k NN estimator of the conditional expectile: Uniform consistency in number of neighbors. Statistics & Risk Modeling with Applications in Finance and Insurance, 2021, ⟨10.1515/strm-2019-0029⟩. ⟨hal-03375532⟩
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