Hierarchical linear regression models for conditional quantiles

DOI10.1007/S11425-006-2023-3zbMath1106.62047OpenAlexW2004086551MaRDI QIDQ870725

Gemai Chen, Mao-Zai Tian

Publication date: 14 March 2007

Published in: Science in China. Series A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11425-006-2023-3

zbMATH Keywords

fixed effectsrandom effectsregression quantileEQ algorithmhierarchical quantile regression models

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20) Linear regression; mixed models (62J05) Applications of statistics to social sciences (62P25)

Related Items (11)

Adaptive quantile regression with precise risk bounds ⋮ General composite quantile regression: Theory and methods ⋮ Linear Quantile Regression Based on EM Algorithm ⋮ Double penalized quantile regression for the linear mixed effects model ⋮ Robust estimation in inverse problems via quantile coupling ⋮ Composite hierachical linear quantile regression ⋮ An effective method to reduce the computational complexity of composite quantile regression ⋮ Adaptive local linear quantile regression ⋮ Bayesian quantile regression for hierarchical linear models ⋮ Semiparametric quantile modelling of hierarchical data ⋮ Semiparametric Hierarchical Composite Quantile Regression

Uses Software

• MIXED
• HLM

Cites Work

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• Estimation in Covariance Components Models
• Regression Quantiles
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