Bahadur representation and its applications for local polynomial estimates in nonparametric M -regression

DOI10.1080/1048525031000089275zbMath1054.62041OpenAlexW2000424197MaRDI QIDQ4470117

Sheng-Yan Hong

Publication date: 22 June 2004

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

Full work available at URL: https://doi.org/10.1080/1048525031000089275

zbMATH Keywords

Bahadur representationRobust regressionAsymptotic normalityLaw of the iterated logarithmLocal polynomial fitting\(M\)-regressionLikelihood regression

Mathematics Subject Classification ID

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

Related Items (10)

A SINGLE-INDEX QUANTILE REGRESSION MODEL AND ITS ESTIMATION ⋮ UNIFORM BIAS STUDY AND BAHADUR REPRESENTATION FOR LOCAL POLYNOMIAL ESTIMATORS OF THE CONDITIONAL QUANTILE FUNCTION ⋮ Global debiased DC estimations for biased estimators via pro forma regression ⋮ Dimension reduction techniques for conditional expectiles ⋮ Expansion for moments of regression quantiles with applications to nonparametric testing ⋮ Bahadur representation for the nonparametricM-estimator under α-mixing dependence ⋮ Bahadur representation of nonparametric \(M\)-estimators for spatial processes ⋮ Wavelet-M-estimation for time-varying coefficient time series models ⋮ UNIFORM BAHADUR REPRESENTATION FOR LOCAL POLYNOMIAL ESTIMATES OF M-REGRESSION AND ITS APPLICATION TO THE ADDITIVE MODEL ⋮ Single index quantile regression for heteroscedastic data

Cites Work

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• A general Bahadur representation of \(M\)-estimators and its application to linear regression with nonstochastic designs
• Local asymptotics for quantile smoothing splines
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• Multivariate locally weighted least squares regression
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• Local linear regression smoothers and their minimax efficiencies
• Robust Locally Weighted Regression and Smoothing Scatterplots
• Design-adaptive Nonparametric Regression
• Generalized Partially Linear Single-Index Models
• A Note on Quantiles in Large Samples

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