Asymptotic properties for l 1 norm kernel estimator of conditional median under dependence

DOI10.1080/1048525031000089293zbMath1054.62050OpenAlexW2024882813MaRDI QIDQ4470115

Hua Liang, Yong Zhou

Publication date: 22 June 2004

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

Full work available at URL: https://doi.org/10.1080/1048525031000089293

zbMATH Keywords

\(\beta\)-mixing\(L_1\)-normKernel estimator\(\alpha\)-mixingConditional median

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) General nonlinear regression (62J02)

Related Items (5)

Nonparametric prediction by conditional median and quantiles ⋮ Semiparametrically Efficient Inference Based on Signs and Ranks for Median-Restricted Models ⋮ Prediction in moving average processes ⋮ Strong uniform consistency rates of conditional quantile estimation in the single functional index model under random censorship ⋮ CLT for single functional index quantile regression under dependence structure

Cites Work

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• Uniform consistency of a class of regression function estimators
• Kernel and nearest-neighbor estimation of a conditional quantile
• A law of the iterated logarithm for nonparametric regression function estimators
• Conditional empirical processes
• Asymptotic properties of kernel estimators based on local medians
• On the almost everywhere convergence of nonparametric regression function estimates
• Absolute regularity and functions of Markov chains
• Nonparametric function estimation involving time series
• A law of the iterated logarithm for \(L_ 1\)-norm kernel estimator of the conditional median

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