Simultaneous estimation and variable selection in median regression using Lasso-type penalty

DOI10.1007/s10463-008-0184-2zbMath1440.62280OpenAlexW2049639446WikidataQ37109516 ScholiaQ37109516MaRDI QIDQ904101

Jinfeng Xu, Zhiliang Ying

Publication date: 15 January 2016

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3749002

zbMATH Keywords

perturbationBayesian information criterionvariable selectionLassoleast absolute deviationsmedian regression

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Asymptotic distribution theory in statistics (62E20) Linear regression; mixed models (62J05)

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Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• The Gaussian hare and the Laplacian tortoise: computability of squared-error versus absolute-error estimators. With comments by Ronald A. Thisted and M. R. Osborne and a rejoinder by the authors
• Asymptotics for Lasso-type estimators.
• Variable selection for Cox's proportional hazards model and frailty model
• Least angle regression. (With discussion)
• On the ``degrees of freedom of the lasso
• Simulation and the Asymptotics of Optimization Estimators
• A Robust Version of Mallows's C p
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Adaptive Model Selection

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