FDA: theoretical and practical efficiency of the local linear estimation based on the kNN smoothing of the conditional distribution when there are missing data

DOI10.1080/00949655.2020.1732378zbMath1495.62122OpenAlexW3012416815MaRDI QIDQ5107786

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

Publication date: 28 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2020.1732378

zbMATH Keywords

functional data analysis (FDA)almost-complete convergence\(k\)-nearest neighbours (\(k\)NN) methodconditional distribution function (CDF)local linear estimation (LLE)missing data at random (MAR)nonparametric functional data analysis (NFDA)

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Density estimation (62G07) Asymptotic distribution theory in statistics (62E20) Asymptotic properties of nonparametric inference (62G20) Functional data analysis (62R10) Nonparametric robustness (62G35) Order statistics; empirical distribution functions (62G30) Statistics of extreme values; tail inference (62G32)

Related Items (3)

The $k$ nearest neighbors local linear estimator of functional conditional density when there are missing data ⋮ k‐Nearest neighbors local linear regression for functional and missing data at random ⋮ Non parametric estimations of the conditional density and mode when the regressor and the response are curves

Uses Software

• fda (R)

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