Weighted composite quantile regression analysis for nonignorable missing data using nonresponse instrument

DOI10.1080/10485252.2017.1285030zbMath1369.62111OpenAlexW2586270687MaRDI QIDQ5266561

Hui Zhao, Pu-Ying Zhao, Zhaohai Li, Nian Sheng Tang

Publication date: 16 June 2017

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

Full work available at URL: https://doi.org/10.1080/10485252.2017.1285030

zbMATH Keywords

empirical likelihoodvariable selectioncomposite quantile regressionnonignorable missing datalocal identification

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Nonparametric regression and quantile regression (62G08) Nonparametric robustness (62G35) Robustness and adaptive procedures (parametric inference) (62F35)

Related Items (3)

Empirical likelihood in varying-coefficient quantile regression with missing observations ⋮ Regularized quantile regression for ultrahigh-dimensional data with nonignorable missing responses ⋮ Improved multiple quantile regression estimation with nonignorable dropouts

Cites Work

• Weighted local linear CQR for varying-coefficient models with missing covariates
• Empirical likelihood ratio confidence regions
• Composite quantile regression and the oracle model selection theory
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• Empirical likelihood and general estimating equations
• A propensity score adjustment method for regression models with nonignorable missing covariates
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• Using calibration weighting to adjust for nonresponse under a plausible model
• Missing Covariates in Generalized Linear Models When the Missing Data Mechanism is Non-ignorable
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Estimation With Survey Data Under Nonignorable Nonresponse or Informative Sampling
• Empirical likelihood estimation for finite populations and the effective usage of auxiliary information
• A Semiparametric Estimation of Mean Functionals With Nonignorable Missing Data
• Semiparametric Pseudo-Likelihoods in Generalized Linear Models With Nonignorable Missing Data
• Identification in Parametric Models

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