On the oracle property of adaptive group Lasso in high-dimensional linear models

DOI10.1007/s00362-015-0684-0zbMath1364.62180OpenAlexW2035989925MaRDI QIDQ259684

Caiya Zhang, Yanbiao Xiang

Publication date: 18 March 2016

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-015-0684-0

zbMATH Keywords

high dimensionalityoracle propertyadaptive group Lassoregression model

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Ridge regression; shrinkage estimators (Lasso) (62J07) Linear regression; mixed models (62J05)

Related Items (9)

Adaptive group Lasso selection in quantile models ⋮ Automatic selection by penalized asymmetric L _q -norm in a high-dimensional model with grouped variables ⋮ Detection of similar successive groups in a model with diverging number of variable groups ⋮ Adaptive group Lasso for high-dimensional generalized linear models ⋮ Variable selection in partially linear additive hazards model with grouped covariates and a diverging number of parameters ⋮ Degrees of freedom for regularized regression with Huber loss and linear constraints ⋮ The generalized equivalence of regularization and min-max robustification in linear mixed models ⋮ Adaptive fused LASSO in grouped quantile regression ⋮ Adaptive elastic-net selection in a quantile model with diverging number of variable groups

Cites Work

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• On the asymptotic properties of the group lasso estimator for linear models
• On the adaptive elastic net with a diverging number of parameters
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models
• Convergence and sparsity of Lasso and group Lasso in high-dimensional generalized linear models
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
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