M-estimation in linear models under nonstandard conditions.

DOI10.1016/S0378-3758(03)00113-7zbMath1038.62026OpenAlexW2091661246MaRDI QIDQ1427512

Faouzi El Bantli

Publication date: 14 March 2004

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0378-3758(03)00113-7

zbMATH Keywords

M-estimatorsBootstrapLimiting distributionLinear model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05) Bootstrap, jackknife and other resampling methods (62F40)

Related Items (3)

General \(M\)-estimator processes and their \(m\) out of \(n\) bootstrap with functional nuisance parameters ⋮ Second order correctness of perturbation bootstrap M-estimator of multiple linear regression parameter ⋮ General \(M\)-estimation and its bootstrap

Uses Software

• bootstrap

Cites Work

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• Cube root asymptotics
• Asymptotics with increasing dimension for robust regression with applications to the bootstrap
• On the multivariate law of the iterated logarithm
• Some asymptotic theory for the bootstrap
• Asymptotics for \(M\)-estimators defined by convex minimization
• Bootstrapping \(M\)-estimators of a multiple linear regression parameter
• Asymptotic relations of M-estimates and R-estimates in linear regression model
• Central limit theorems for empirical measures
• Asymptotic behavior of M-estimators for the linear model
• On the bootstrap of the sample mean in the infinite variance case
• A general Bahadur representation of \(M\)-estimators and its application to linear regression with nonstochastic designs
• General \(M\)-estimation
• An Edgeworth expansion for the \(m\) out of \(n\) bootstrapped median
• Limiting distributions for \(L_1\) regression estimators under general conditions
• Jackknife, bootstrap and other resampling methods in regression analysis
• Robust regression: Asymptotics, conjectures and Monte Carlo
• Large sample confidence regions based on subsamples under minimal assumptions
• Linear representation of M-estimates in linear models
• Regression Quantiles
• LEAST ABSOLUTE DEVIATIONS REGRESSION UNDER NONSTANDARD CONDITIONS

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