A resampling method by perturbing the estimating functions for quantile regression with missing data

DOI10.1080/03610918.2016.1271892zbMath1388.62118OpenAlexW2564293760MaRDI QIDQ4638857

Yong Zhou, Li Zhang, Cun-Jie Lin

Publication date: 30 April 2018

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2016.1271892

zbMATH Keywords

bootstrapquantile regressionmissing dataestimating equationsresampling method

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Censored data models (62N01) Nonparametric statistical resampling methods (62G09)

Cites Work

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• Empirical likelihood for linear models with missing responses
• Data. A collection of problems from many fields for the student and research worker
• Bootstrap methods: another look at the jackknife
• A general Bahadur representation of \(M\)-estimators and its application to linear regression with nonstochastic designs
• Simple resampling methods for censored regression quantiles
• Introduction to empirical processes and semiparametric inference
• Quantile regression for competing risks data with missing cause of failure
• Estimating Equations Inference With Missing Data
• Efficient resampling methods for nonsmooth estimating functions
• Inference and missing data
• Regression Quantiles
• A resampling method based on pivotal estimating functions
• The estimating function bootstrap
• On Single-Index Coefficient Regression Models
• Asymptotics for L₁‐estimators of regression parameters under heteroscedasticityY
• Efficient Quantile Regression Analysis With Missing Observations
• Missing-Data Methods for Generalized Linear Models

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