Consistent model identification of varying coefficient quantile regression with BIC tuning parameter selection

DOI10.1080/03610926.2015.1010009zbMath1360.62404OpenAlexW2314797594WikidataQ37514427 ScholiaQ37514427MaRDI QIDQ2979579

Qi Zheng, Limin Peng

Publication date: 25 April 2017

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

Full work available at URL: http://europepmc.org/articles/pmc5166990

zbMATH Keywords

quantile regressionshrinkage estimationBayesian information criterionvarying covariate effects

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (2)

A penalized approach to covariate selection through quantile regression coefficient models ⋮ Tensor Response Quantile Regression with Neuroimaging Data

Cites Work

• The Adaptive Lasso and Its Oracle Properties
• Parametric component detection and variable selection in varying-coefficient partially linear models
• Composite quantile regression and the oracle model selection theory
• Log log laws for empirical measures
• Adaptive penalized quantile regression for high dimensional data
• New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
• Unified LASSO Estimation by Least Squares Approximation
• Regression Quantiles
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Model Selection via Bayesian Information Criterion for Quantile Regression Models
• Shrinkage Estimation of the Varying Coefficient Model
• Tuning parameter selectors for the smoothly clipped absolute deviation method

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