Weighted quantile regression with missing covariates using empirical likelihood

DOI10.1080/02331888.2015.1033164zbMath1359.62131OpenAlexW2052625113MaRDI QIDQ5739652

Tianqing Liu, Xiaohui Yuan

Publication date: 19 July 2016

Published in: Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331888.2015.1033164

zbMATH Keywords

quantile regressionempirical likelihoodresampling methodinverse probability weightingmissing covariates

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12)

Related Items (8)

Bayesian empirical likelihood inference and order shrinkage for autoregressive models ⋮ Empirical likelihood in varying-coefficient quantile regression with missing observations ⋮ An improvement on the efficiency of complete-case-analysis with nonignorable missing covariate data ⋮ Empirical likelihood inference for rank regression with doubly truncated data ⋮ On histogram-based regression and classification with incomplete data ⋮ Weighted rank estimation of nonparametric transformation models with case-1 and case-2 interval-censored failure time data ⋮ Weighted empirical likelihood for quantile regression with non ignorable missing covariates ⋮ Empirical likelihood weighted composite quantile regression with partially missing covariates

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