FDA: strong consistency of the kNN local linear estimation of the functional conditional density and mode

DOI10.1080/10485252.2018.1538450zbMath1409.62088OpenAlexW2899231528MaRDI QIDQ4613970

Ibrahim M. Almanjahie, Ali Laksaci, Mustapha Rachdi, Zouaoui Chikr Elmezouar

Publication date: 28 January 2019

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485252.2018.1538450

zbMATH Keywords

kernel methodconditional modefunctional data analysislocal linear estimationconditional density functionnonparametric functional data analysis\(k\)-nearest neighbours methodalmost-complete convergence

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20)

Related Items (7)

The $k$ nearest neighbors local linear estimator of functional conditional density when there are missing data ⋮ Regression model for surrogate data in high dimensional statistics ⋮ Computational aspects of the kNN local linear smoothing for some conditional models in high dimensional statistics ⋮ k‐Nearest neighbors local linear regression for functional and missing data at random ⋮ FDA: theoretical and practical efficiency of the local linear estimation based on the kNN smoothing of the conditional distribution when there are missing data ⋮ The \(k\)-nearest neighbors method in single index regression model for functional quasi-associated time series data ⋮ The functional \(k\mathrm{NN}\) estimator of the conditional expectile: uniform consistency in number of neighbors

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