Empirical likelihood weighted composite quantile regression with partially missing covariates
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Publication:5266558
DOI10.1080/10485252.2016.1272692zbMath1365.62145OpenAlexW2566215573MaRDI QIDQ5266558
Publication date: 16 June 2017
Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10485252.2016.1272692
incomplete dataempirical likelihoodcomposite quantile regressioninverse probability weightingunbiased estimating equations
Asymptotic properties of parametric estimators (62F12) Nonparametric regression and quantile regression (62G08) Nonparametric estimation (62G05)
Related Items (3)
Empirical likelihood in varying-coefficient quantile regression with missing observations ⋮ An improvement on the efficiency of complete-case-analysis with nonignorable missing covariate data ⋮ Robust check loss-based inference of semiparametric models and its application in environmental data
Cites Work
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- Weighted local linear CQR for varying-coefficient models with missing covariates
- Composite quantile regression for single-index models with asymmetric errors
- An improved and efficient estimation method for varying-coefficient model with missing covariates
- Composite quantile regression and the oracle model selection theory
- Empirical likelihood and general estimating equations
- A propensity score adjustment method for regression models with nonignorable missing covariates
- New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
- Empirical Likelihood in Missing Data Problems
- Empirical Likelihood for Non‐Smooth Criterion Functions
- Weighted quantile regression with missing covariates using empirical likelihood
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