Nonparametric regression M-quantiles

DOI10.1016/0167-7152(89)90044-8zbMath0682.62022OpenAlexW2001322949MaRDI QIDQ1824314

Paul Janssen, Jaromír Antoch

Publication date: 1989

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-7152(89)90044-8

zbMATH Keywords

asymptotic normalityBahadur representationnonparametric regressionkernel type estimatorsM-smoothersregression M-quantilesstrong consistency rate

Mathematics Subject Classification ID

Inequalities; stochastic orderings (60E15) Nonparametric estimation (62G05) General nonlinear regression (62J02)

Related Items (7)

Estimation of the tail-index in a conditional location-scale family of heavy-tailed distributions ⋮ Conditional \(L_ p\)-quantiles and their application to the testing of symmetry in non-parametric regression ⋮ On \(M\)-estimators and normal quantiles. ⋮ A convergent algorithm for quantile regression with smoothing splines ⋮ Asymptotic representation theory for nonstandard conditional quantiles ⋮ On estimating conditional quantiles and distribution functions. ⋮ Exactly what is being modelled by the systematic component in a heteroscedastic linear regression

Cites Work

• Nonparametric regression: An up–to–date bibliography
• On nonparametric regression estimators based on regression quantiles
• Estimation Non-paramétrique de la Régression: Revue Bibliographique
• A New Proof of the Bahadur Representation of Quantiles and an Application
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