Non Parametric Regression Quantile Estimation for Dependent Functional Data under Random Censorship: Asymptotic Normality

DOI10.1080/03610926.2013.784993zbMath1333.62101OpenAlexW2047096136MaRDI QIDQ2796935

Walid Horrigue, Elias Ould Saïd

Publication date: 30 March 2016

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2013.784993

zbMATH Keywords

asymptotic normalitystrong mixingkernel estimatorcensored datafunctional dataconditional distribution functionregression quantilesmall balls probability

Mathematics Subject Classification ID

Density estimation (62G07) Nonparametric estimation (62G05) Non-Markovian processes: estimation (62M09)

Related Items

Nonparametric local linear regression estimation for censored data and functional regressors ⋮ Consistency result of recursive conditional distribution estimate for dependent data under left truncation, with applications to the conditional quantile ⋮ Unnamed Item ⋮ Local linear estimation of the conditional cumulative distribution function: censored functional data case ⋮ Robust regression analysis for a censored response and functional regressors ⋮ Unnamed Item ⋮ Strong convergence of the functional nonparametric relative error regression estimator under right censoring ⋮ Functional data analysis: estimation of the relative error in functional regression under random left-truncation model ⋮ On the nonparametric estimation of the functional \(\psi\)-regression for a random left-truncation model

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