Lasso with convex loss: Model selection consistency and estimation

DOI10.1080/03610926.2013.870799zbMath1341.62236OpenAlexW2299306323MaRDI QIDQ2811411

Wojciech Rejchel

Publication date: 10 June 2016

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

Full work available at URL: https://doi.org/10.1080/03610926.2013.870799

zbMATH Keywords

model selectionoracle propertyLassoadaptive Lassoconvex loss function

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07)

Related Items (1)

Budget constrained model selection for multiple linear regression

Cites Work

• The Adaptive Lasso and Its Oracle Properties
• Rank correlation estimators and their limiting distributions
• A method of synthesis of linear discriminant function in the case of nonseparability
• Concavity and estimation
• Asymptotics for \(M\)-estimators defined by convex minimization
• Weak convergence of convex stochastic processes
• Bahadur representation of \(M_m\) estimates
• Asymptotics for Lasso-type estimators.
• On the asymptotics of constrained \(M\)-estimation
• Ranking and empirical minimization of \(U\)-statistics
• High-dimensional graphs and variable selection with the Lasso
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Asymptotic Stochastic Programs
• A unified framework for high-dimensional analysis of \(M\)-estimators with decomposable regularizers

This page was built for publication: Lasso with convex loss: Model selection consistency and estimation