Penalized empirical likelihood for quantile regression with missing covariates and auxiliary information

DOI10.1080/03610926.2017.1335413zbMath1392.62123OpenAlexW2624316521MaRDI QIDQ4563514

Yu Shen, Guo-Liang Fan, Han-Ying Liang

Publication date: 1 June 2018

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610926.2017.1335413

zbMATH Keywords

quantile regressionmissing at randomvariable selectionauxiliary informationpenalized empirical likelihood

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

Related Items (4)

Empirical likelihood in varying-coefficient quantile regression with missing observations ⋮ Empirical likelihood for generalized functional-coefficient regression models with multiple smoothing variables under right censoring data ⋮ Robust penalized empirical likelihood estimation method for linear regression ⋮ An elastic-net penalized expectile regression with applications

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