A new model selection procedure based on dynamic quantile regression

DOI10.1080/02664763.2014.909787zbMath1352.62025OpenAlexW2061102739MaRDI QIDQ2953289

Wei Xiong, Mao-Zai Tian

Publication date: 4 January 2017

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2014.909787

zbMATH Keywords

asymptotic relative efficiencyvariable selectionheteroskedasticityerror distributiondynamic quantile regressionDQR-oracle

Mathematics Subject Classification ID

General nonlinear regression (62J02)

Cites Work

• The Adaptive Lasso and Its Oracle Properties
• Composite quantile regression and the oracle model selection theory
• Rejoinder: One-step sparse estimates in nonconcave penalized likelihood models
• New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Tuning parameter selectors for the smoothly clipped absolute deviation method

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